Screening for Small Molecules to Alter Calpain Proteolysis through Modulating Conformation Changes Induced by Heterodimerization.

Dysregulated calpain-1 and calpain-2 protease activity linked to several diseases has encouraged efforts to explore inhibiting calpain to provide therapeutic benefits. However, there are currently no clinically approved drugs that specifically target calpain functionality. To address this unmet need, we carried out drug discovery efforts to identify small molecules capable of modulating calpain activity.

Quantification and structure-function analysis of calpain-1 and calpain-2 protease subunit interactions.

Calpain-1 and calpain-2 are heterodimeric proteases consisting of a common small regulatory subunit CAPNS1 and a large catalytic subunit, CAPN1 or CAPN2, respectively. These calpains have emerged as potential therapeutic targets in cancer and other diseases through their roles in cell signaling pathways affecting sensitivity to chemotherapeutic and targeted drugs and in promoting metastasis. While inhibition of calpains has the potential to provide therapeutic benefit to cancer patients, there are currently no clinically approved active site-directed drugs that specifically and effectively inhibit them.

Assessing the predicted impact of single amino acid substitutions in MAPK proteins for CAGI6 challenges.

New thermodynamic and functional studies have been recently conducted to evaluate the impact of amino acid substitutions on the Mitogen Activated Protein Kinases 1 and 3 (MAPK1/3). The Critical Assessment of Genome Interpretation (CAGI) data provider, at Sapienza University of Rome, measured the unfolding free energy and the enzymatic activity of a set of variants (MAPK challenge dataset). Thermodynamic measurements for the denaturant-induced equilibrium unfolding of the phosphorylated and unphosphorylated forms of the MAPKs were obtained by monitoring the far-UV circular dichroism and intrinsic fluorescence changes as a function of denaturant concentration.

Further Development of SAMPDI-3D: A Machine Learning Method for Predicting Binding Free Energy Changes Caused by Mutations in Either Protein or DNA.

Background/objectives: Predicting the effects of protein and DNA mutations on the binding free energy of protein-DNA complexes is crucial for understanding how DNA variants impact wild-type cellular function. As many cellular interactions involve protein-DNA binding, accurately predicting changes in binding free energy (ΔΔG) is valuable for distinguishing pathogenic mutations from benign ones.

Methods: This study describes the development and optimization of the SAMPDI-3Dv2 machine learning method, which is trained on an expanded database of experimentally measured ΔΔGs.

Assessing the predicted impact of single amino acid substitutions in calmodulin for CAGI6 challenges.

Recent thermodynamic and functional studies have been conducted to evaluate the impact of amino acid substitutions on Calmodulin (CaM). The Critical Assessment of Genome Interpretation (CAGI) data provider at University of Verona (Italy) measured the melting temperature (T) and the percentage of unfolding (%unfold) of a set of CaM variants (CaM challenge dataset). Thermodynamic measurements for the equilibrium unfolding of CaM were obtained by monitoring far-UV Circular Dichroism as a function of temperature.

Genetic Variants Linked to Opioid Addiction: A Genome-Wide Association Study.

Opioid use disorder (OUD) affects millions of people worldwide. While it is known that OUD originates from many factors, including social and environmental factors, the role of genetic variants in developing the disease has also been reported. This study aims to investigate the genetic variants associated with the risk of developing OUD upon exposure.

On delivering polar solvation free energy of proteins from energy minimized structures using a regularized super-Gaussian Poisson-Boltzmann model.

The biomolecules interact with their partners in an aqueous media; thus, their solvation energy is an important thermodynamics quantity. In previous works (J. Chem.

SAAMBE-MEM: a sequence-based method for predicting binding free energy change upon mutation in membrane protein-protein complexes.

Motivation: Mutations in protein-protein interactions can affect the corresponding complexes, impacting function and potentially leading to disease. Given the abundance of membrane proteins, it is crucial to assess the impact of mutations on the binding affinity of these proteins. Although several methods exist to predict the binding free energy change due to mutations in protein-protein complexes, most require structural information of the protein complex and are primarily trained on the SKEMPI database, which is composed mainly of soluble proteins.

Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations.

The development of methods and algorithms to predict the effect of mutations on protein stability, protein-protein interaction, and protein-DNA/RNA binding is necessitated by the needs of protein engineering and for understanding the molecular mechanism of disease-causing variants. The vast majority of the leading methods require a database of experimentally measured folding and binding free energy changes for training. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residues on the above-mentioned thermodynamic characteristics, i.

Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation.

Here, we present a Gaussian-based method for estimation of protein-protein binding entropy to augment the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method for computational prediction of binding free energy (Δ). The method is termed f5-MM/PBSA/E, where "E" stands for entropy and f5 for five adjustable parameters. The enthalpy components of Δ (molecular mechanics, polar and non-polar solvation energies) are computed from a single implicit solvent generalized Born (GB) energy minimized structure of a protein-protein complex, while the binding entropy is computed using independently GB energy minimized unbound and bound structures.

SAMPDI-3D: predicting the effects of protein and DNA mutations on protein-DNA interactions.

Motivation: Mutations that alter protein-DNA interactions may be pathogenic and cause diseases. Therefore, it is extremely important to quantify the effect of mutations on protein-DNA binding free energy to reveal the molecular origin of diseases and to assist the development of treatments. Although several methods that predict the change of protein-DNA binding affinity upon mutations in the binding protein were developed, the effect of DNA mutations was not considered yet.

SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability.

Modeling the effect of mutations on protein thermodynamics stability is useful for protein engineering and understanding molecular mechanisms of disease-causing variants. Here, we report a new development of the SAAFEC method, the SAAFEC-SEQ, which is a gradient boosting decision tree machine learning method to predict the change of the folding free energy caused by amino acid substitutions. The method does not require the 3D structure of the corresponding protein, but only its sequence and, thus, can be applied on genome-scale investigations where structural information is very sparse.

BION-2: Predicting Positions of Non-Specifically Bound Ions on Protein Surface by a Gaussian-Based Treatment of Electrostatics.

Ions play significant roles in biological processes-they may specifically bind to a protein site or bind non-specifically on its surface. Although the role of specifically bound ions ranges from actively providing structural compactness via coordination of charge-charge interactions to numerous enzymatic activities, non-specifically surface-bound ions are also crucial to maintaining a protein's stability, responding to pH and ion concentration changes, and contributing to other biological processes. However, the experimental determination of the positions of non-specifically bound ions is not trivial, since they may have a low residential time and experience significant thermal fluctuation of their positions.

Application and Comprehensive Analysis of Neighbor Approximated Information Theoretic Configurational Entropy Methods to Protein-Ligand Binding Cases.

The binding entropy is an important thermodynamic quantity which has numerous applications in studies of the biophysical process, and configurational entropy is often one of the major contributors in it. Therefore, its accurate estimation is important, though it is challenging mostly due to sampling limitations, anharmonicity, and multimodality of atomic fluctuations. The present work reports a Neighbor Approximated Maximum Information Spanning Tree (A-MIST) method for conformational entropy and presents its performance and computational advantage over conventional Mutual Information Expansion (MIE) and Maximum Information Spanning Tree (MIST) for two protein-ligand binding cases: indirubin-5-sulfonate to Protein Kinase 5 (PfPK5) and RON2-peptide to Apical Membrane Antigen 1 (PfAMA1).

DelPhi Suite: New Developments and Review of Functionalities.

Electrostatic potential, energies, and forces affect virtually any process in molecular biology, however, computing these quantities is a difficult task due to irregularly shaped macromolecules and the presence of water. Here, we report a new edition of the popular software package DelPhi along with describing its functionalities. The new DelPhi is a C++ object-oriented package supporting various levels of multiprocessing and memory distribution.

Coupling Supervised Molecular Dynamics (SuMD) with Entropy Estimations To Shine Light on the Stability of Multiple Binding Sites.

Exploring at the molecular level, all possible ligand-protein approaching pathways and, consequently, identifying the energetically favorable binding sites is considered crucial to depict a clear picture of the whole scenario of ligand-protein binding. In fact, a ligand can recognize a protein in multiple binding sites, adopting multiple conformations in every single binding site and inducing protein modifications upon binding. In the present work, we would like to present how it is possible to couple a supervised molecular dynamics (SuMD) approach to explore, from an unbound state, the most energetically favorable recognition pathways of the ligand to its protein, with an enthalpic and entropic characterization of the most stable ligand-protein bound states, using the protein kinase CK2α as a prototype study.

ProLego: tool for extracting and visualizing topological modules in protein structures.

Background: In protein design, correct use of topology is among the initial and most critical feature. Meticulous selection of backbone topology aids in drastically reducing the structure search space. With ProLego, we present a server application to explore the component aspect of protein structures and provide an intuitive and efficient way to scan the protein topology space.