Exploring the composition of protein-ligand binding sites for cancerous inhibitor of PP2A (CIP2A) by inhibitor guided binding analysis: paving a new way for the Discovery of drug candidates against triple negative breast cancer (TNBC).

Triple-negative breast cancer (TNBC) is associated with high-grade invasive carcinoma leading to a 10% to 15% death rate in younger premenopausal women. Targeting cancerous inhibitors of protein phosphatase (CIP2A) has been a highly effective approach for exploring therapeutic drug candidates. Lapatinib, a dual tyrosine kinase inhibitor, has shown promising inhibition properties by inducing apoptosis in TNBC carcinogenesis in vivo. Despite knowledge of the 3D structure of CIP2A, no reports provide insight into CIP2A ligand binding sites. To this effect, we conducted in silico site identification guided by lapatinib binding. Four of the five sites identified were cross-validated, and the stem domain revealed more excellent ligand binding affinity. The binding affinity of lapatinib in these sites was further computed using the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) approach. According to MM/PBSA//200 ns MD simulations, lapatinib exhibited a higher binding affinity against CIP2A in site 2 with ΔG critical values of -37.1 kcal/mol. The steadiness and tightness of lapatinib with CIP2A inside the stem domain disclosed glutamic acid-318 as the culprit amino acid with the highest electrostatic energy. These results provide clear information on the CIP2A domain capable of ligand binding and validate lapatinib as a promising CIP2A inhibitor in TNBC carcinogenesis.

Machine Learning in Enhancing Protein Binding Sites Predictions - What Has Changed Since Then?

Accurate identification of protein binding sites is pivotal for understanding molecular interactions and facilitating drug discovery efforts. However, the dynamic nature of proteinligand interactions presents a formidable challenge, necessitating innovative approaches to bridge the gap between theoretical predictions and experimental realities. This review explores the challenges and recent advancements in protein binding site prediction. Specifically, we highlight the integration of molecular dynamics simulations, machine learning, and deep learning techniques to capture the dynamic and complex nature of protein-ligand interactions. Additionally, we discuss the importance of integrating experimental data, such as structural information and biochemical assays, to enhance prediction accuracy and reliability. By navigating the intersection of classical and the onset of machine learning and deep learning approaches, we aim to provide insights into current state-of-the-art techniques and chart a course for future protein binding site prediction advancements. Ultimately, these efforts could unravel the mysteries of protein-ligand interactions and accelerate drug discovery endeavors.