In silico identification of inhibitors targeting N-Terminal domain of human Replication Protein A

In silico approaches for drug repurposing in oncology: a scoping review

Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.

Replication protein A: a multifunctional protein with roles in DNA replication, repair and beyond

Single-stranded DNA (ssDNA) forms continuously during DNA replication and is an important intermediate during recombination-mediated repair of damaged DNA. Replication protein A (RPA) is the major eukaryotic ssDNA-binding protein. As such, RPA protects the transiently formed ssDNA from nucleolytic degradation and serves as a physical platform for the recruitment of DNA damage response factors. Prominent and well-studied RPA-interacting partners are the tumor suppressor protein p53, the RAD51 recombinase and the ATR-interacting proteins ATRIP and ETAA1. RPA interactions are also documented with the helicases BLM, WRN and SMARCAL1/HARP, as well as the nucleotide excision repair proteins XPA, XPG and XPF–ERCC1. Besides its well-studied roles in DNA replication (restart) and repair, accumulating evidence shows that RPA is engaged in DNA activities in a broader biological context, including nucleosome assembly on nascent chromatin, regulation of gene expression, telomere maintenance and numerous other aspects of nucleic acid metabolism. In addition, novel RPA inhibitors show promising effects in cancer treatment, as single agents or in combination with chemotherapeutics. Since the biochemical properties of RPA and its roles in DNA repair have been extensively reviewed, here we focus on recent discoveries describing several non-canonical functions.

Comprehensive evaluation of the MM-GBSA method on bromodomain-inhibitor sets

MM-PB/GBSA methods represent a higher-level scoring theory than docking. This study reports an extensive testing of different MM-GBSA scoring schemes on two bromodomain (BRD) datasets. The first set is composed of 24 BRPF1 complexes, and the second one is a nonredundant set constructed from the PDBbind and composed of 28 diverse BRD complexes. A variety of MM-GBSA schemes were analyzed to evaluate the performance of four protocols with different numbers of minimization and MD steps, 10 different force fields and three different water models. Results showed that neither additional MD steps nor unfixing the receptor atoms improved scoring or ranking power. On the contrary, our results underscore the advantage of fixing receptor atoms or limiting the number of MD steps not only for a reduction in the computational costs but also for boosting the prediction accuracy. Among Amber force fields tested, ff14SB and its derivatives rather than ff94 or polarized force fields provided the most accurate scoring and ranking results. The TIP3P water model yielded the highest scoring and ranking power compared to the others. Posing power was further evaluated for the BRPF1 set. A slightly better posing power for the protocol which uses both minimization and MD steps with a fixed receptor than the one which uses only minimization with a fully flexible receptor-ligand system was observed. Overall, this study provides insights into the usage of the MM-GBSA methods for screening of BRD inhibitors, substantiating the benefits of shorter protocols and latest force fields and maintaining the crystal waters for accuracy.

Comparative Assessment of Seven Docking Programs on a Nonredundant Metalloprotein Subset of the PDBbind Refined

Extensive usage of molecular docking for computer-aided drug discovery resulted in development of numerous programs with versatile scoring and posing algorithms. Selection of the docking program among these vast number of options is central to the outcome of drug discovery. To this end, comparative assessment studies of docking offer valuable insights into the selection of the optimal tool. Despite the availability of various docking assessment studies, the performance difference of docking programs has not been well addressed on metalloproteins which comprise a substantial portion of the human proteome and have been increasingly targeted for treatment of a wide variety of diseases. This study reports comparative assessment of seven docking programs on a diverse metalloprotein set which was compiled for this study. The refined set of the PDBbind (2017) was screened to gather 710 complexes with metal ion(s) closely located to the ligands (<4 Å). The redundancy was eliminated by clustering and overall 213 complexes were compiled as the nonredundant metalloprotein subset of the PDBbind refined. The scoring, ranking, and posing powers of seven noncommercial docking programs, namely, AutoDock4, AutoDock4_Zn, AutoDock Vina, Quick Vina 2, LeDock, PLANTS, and UCSF DOCK6, were comprehensively evaluated on this nonredundant set. Results indicated that PLANTS (80%) followed by LeDock (77%), QVina (76%), and Vina (73%) had the most accurate posing algorithms while AutoDock4 (48%) and DOCK6 (56%) were the least successful in posing. Contrary to their moderate-to-high level of posing success, none of the programs was successful in scoring or ranking of the binding affinities (r² ≈ 0). Screening power was further evaluated by using active-decoy ligand sets for a large compilation of metalloprotein targets. PLANTS stood out among other programs to be able to enrich the active ligand for every target, underscoring its robustness for screening of metalloprotein inhibitors. This study provides useful information for drug discovery studies targeting metalloproteins.