Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set.

Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)-ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across receptor binding domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP). These predictors were selected based on their user-friendliness, accessibility, and speed. As a result of our benchmarking efforts, we observed that none of the methods could generate a meaningful correlation with the experimental binding data. The best correlation is achieved by FoldX (R = -0.51). When we simplified the prediction problem to a binary classification, that is, whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with a 64% success rate. Surprisingly, on this set, simple energetic scoring functions performed significantly better than the ones using extra evolutionary-based terms, as in Mutabind and SSIPe. Furthermore, we demonstrated that recent AI approaches, mmCSM-PPI and TopNetTree, yielded comparable performances to the force field-based techniques. These observations suggest plenty of room to improve the binding affinity predictors in guessing the variant-induced binding profile changes of a host-pathogen system, such as Spike-ACE2. To aid such improvements we provide our benchmarking data at https://github.com/CSB-KaracaLab/RBD-ACE2-MutBench with the option to visualize our mutant models at https://rbd-ace2-mutbench.github.io/.

Identification of PPA1 inhibitor candidates for potential repurposing in cancer medicine.

Inorganic pyrophosphatase 1 (PPA1) is pivotal to cellular metabolism as it facilitates the hydrolysis of PPi-a by-product of various metabolic processes that influence cell growth and differentiation. Overexpression of PPA1 enzyme has been linked to diminished patient survival and was shown to influence tumor cell dynamics, thereby positioning it as a potential therapy target for a variety of cancers including colorectal cancer, diffuse large B-cell lymphoma, and lung adenocarcinoma. Despite this therapeutic promise, there are no known inhibitors of PPA1 as of today. In this study, we searched for potential PPA1 inhibitors using a molecular docking screen of 30 470 compounds with a history of clinical trials and/or US Food and Drug Administration approval. We specifically targeted the active pocket that coincides with the established catalytic domain. Our screen identified promising hits, which we further subjected to ADMET (absorption, distribution, metabolism, excretion, and toxicity) filtering. Subsequent molecular dynamics (MD) analyses were conducted on devazepide, quinotolast, and tarazepide-the three substances that successfully navigated all filters. MD analyses reinforced the stability of the protein-ligand complexes and confirmed ligand binding, as substantiated by our root mean square deviation, radius of gyration and secondary structures of proteins analyses. Furthermore, Molecular Mechanics Poisson-Boltzmann Surface Area calculations post-MD identified devazepide and quinotolast as showing higher binding affinities; being supported by principal component analysis, free energy landscape, and dynamic cross-correlation matrix results. Overall, our study reveals devazepide and quinotolast as potential candidates for PPA1 inhibition which could be considered for repurposing studies that need further experimental validation. These results not only reveal a potential for clinical repurposing for PPA1 inhibition but they also offer valuable insights into the development of future compounds for targeting the crucial PPA1 enzyme.

In silico drug screen reveals potential competitive MTHFR inhibitors for clinical repurposing.

MTHFR (Methylenetetrahydrofolate reductase) is a pivotal enzyme involved in one-carbon metabolism, which is critical for the proliferation of cancer cells. In line with this, published literature showed that MTHFR knockdown caused impaired growth of multiple types of cancer cells. Moreover, higher MTHFR expression levels were linked to shorter overall survival in hepatocellular carcinoma, adrenocortical carcinoma, and low-grade glioma, bringing the need to design MTHFR inhibitors as a possible treatment option. No competitive inhibitors of MTHFR have been reported as of today. This study aimed to identify potential competitive MTHFR inhibitor candidates using an in silico drug screen. A total of 30470 molecules containing biogenic compounds, FDA-approved drugs, and those in clinical trials were screened against the catalytic pocket of MTHFR in the presence and absence of cofactors. Binding energy and ADMET analysis revealed that Vilanterol (ß2-adrenergic agonist), Selexipag (prostacyclin receptor agonist), and Ramipril Diketopiperazine (ACE inhibitor) are potential competitive inhibitors of MTHFR. Molecular dynamics analyses and MM-PBSA calculations with these compounds particularly revealed the amino acids between 285-290 for ligand binding and highlighted Vilanterol as the strongest candidate for MTHFR inhibition. Our results could guide the development of novel MTHFR inhibitor compounds, which could be inspired by the drugs brought into the spotlight here. More importantly, these potential candidates could be quhickly tested as a repurposing strategy in pre-clinical and clinical studies of the cancers mentioned above.Communicated by Ramaswamy H. Sarma.

Comparative molecular dynamics analyses on PIK3CA hotspot mutations with PI3Kα specific inhibitors and ATP.

PI3K pathway is heavily emphasized in cancer where PIK3CA, which encodes for the p110α subunit of PI3Kα, presents itself as the second most common mutated gene. A lot of effort has been put in developing PI3K inhibitors, opening promising avenues for the treatment of cancer. Among these, PI3Kα specific inhibitor alpelisib was approved by FDA for breast cancer and other α-isoform specific inhibitors such as inavolisib and serabelisib reached clinical trials. However, the mode of action of these inhibitors on mutated PI3Kα and how they interact with mutant structures has not been fully elucidated yet. In this study, we are revealing the calculated interactions and binding affinities of these inhibitors within the context of PIK3CA hotspot mutations (E542K, E545K and H1047R) by employing molecular dynamics (MD) simulations. We performed principal component analysis to understand the motions of the protein complex during our simulations and also checked the correlated motions of all amino acids. Binding affinity calculations with MM-PBSA confirmed the consistent binding of alpelisib across mutations and revealed relatively higher affinities for inavolisib towards wild-type and H1047R mutant structures in comparison to other inhibitors. On the other hand, E542K mutation significantly impaired the interaction of inavolisib and serabelisib with PI3Kα. We also investigated the structural relationship of the natural ligand ATP with PI3Kα, and interestingly realized a significant reduction in binding affinity for the mutants, with potentially unexpected implications on the mechanisms that render these mutations oncogenic. Moreover, correlated motions of all residues were generally higher for ATP except the H1047R mutation which exhibited a distinguishable reduction. The results presented here could be guiding for pre-clinical and clinical studies of personalized medicine where individual mutations are a strong consideration point.