Lysozyme allosteric interactions with β-blocker drugs

Effective and reliable prediction of allosteric molecular interactions involved in protein-ligand systems are essential to understand pharmacological modulation and toxicology processes that are driven by multiple factors covering from the atomistic to cellular level. Even though the interactions taking place within a defined biophysical environment are usually intricate and complex, having a preliminary knowledge of the structural determinant and biochemical function of target enzyme in the physiological or unbound state represent a step forward in the characterization of the forces involved these processes under interaction conditions as induced by drugs. In the present work, we tackle the study of relevant binding interactions between two well-recognized betablocker drugs and the lysozyme biological target from an experimental-computational perspective. In this way, molecular docking, machine learning and perturbation analysis combined with UV–vis and fluorescence measurements will allow us to determine the allosteric regulation and functional dynamics of lysozyme by binding propranolol and acebutololThe authors acknowledge Ministerio de Ciencia e Innovación (PID2019-111327GB-100)S

Dissecting Structure-Encoded Determinants of Allosteric Cross-Talk between Post-Translational Modification Sites in the Hsp90 Chaperones

Post-translational modifications (PTMs) represent an important regulatory instrument that modulates structure, dynamics and function of proteins. The large number of PTM sites in the Hsp90 proteins that are scattered throughout different domains indicated that synchronization of multiple PTMs through a combinatorial code can be invoked as an important mechanism to orchestrate diverse chaperone functions and recognize multiple client proteins. In this study, we have combined structural and coevolutionary analysis with molecular simulations and perturbation response scanning analysis of the Hsp90 structures to characterize functional role of PTM sites in allosteric regulation. The results reveal a small group of conserved PTMs that act as global mediators of collective dynamics and allosteric communications in the Hsp90 structures, while the majority of flexible PTM sites serve as sensors and carriers of the allosteric structural changes. This study provides a comprehensive structural, dynamic and network analysis of PTM sites across Hsp90 proteins, identifying specific role of regulatory PTM hotspots in the allosteric mechanism of the Hsp90 cycle. We argue that plasticity of a combinatorial PTM code in the Hsp90 may be enacted through allosteric coupling between effector and sensor PTM residues, which would allow for timely response to structural requirements of multiple modified enzymes

Development of Integrated Machine Learning and Data Science Approaches for the Prediction of Cancer Mutation and Autonomous Drug Discovery of Anti-Cancer Therapeutic Agents

Few technological ideas have captivated the minds of biochemical researchers to the degree that machine learning (ML) and artificial intelligence (AI) have. Over the last few years, advances in the ML field have driven the design of new computational systems that improve with experience and are able to model increasingly complex chemical and biological phenomena. In this dissertation, we capitalize on these achievements and use machine learning to study drug receptor sites and design drugs to target these sites. First, we analyze the significance of various single nucleotide variations and assess their rate of contribution to cancer. Following that, we used a portfolio of machine learning and data science approaches to design new drugs to target protein kinase inhibitors. We show that these techniques exhibit strong promise in aiding cancer research and drug discovery