Novel pH-dependent regulation of human cytosolic sialidase 2 (NEU2) activities by siastatin B and structural prediction of NEU2/siastatin B complex

Human cytosolic sialidase (Neuraminidase 2, NEU2) catalyzes the removal of terminal sialic acid residues from glycoconjugates. The effect of siastatin B, known as a sialidase inhibitor, has not been evaluated toward human NEU2 yet. We studied the regulation of NEU2 activity by siastatin B in vitro and predicted the interaction in silico. Inhibitory and stabilizing effects of siastatin B were analyzed in comparison with DANA (2-deoxy-2,3-dehydro-N-acetylneuraminic acid) toward 4-umbelliferyl N-acetylneuraminic acid (4-MU-NANA)- and α2,3-sialyllactose-degrading activities of recombinant NEU2 produced by E. coli GST-fusion gene expression. Siastatin B exhibited to have higher competitive inhibitory activity toward NEU2 than DANA at pH 4.0. We also revealed the stabilizing effect of siastatin B toward NEU2 activity at acidic pH. Docking model was constructed on the basis of the crystal structure of NEU2/DANA complex (PDB code: 1VCU). Molecular docking predicted that electrostatic neutralization of E111 and E218 residues of the active pocket should not prevent siastatin B from binding at pH 4.0. The imino group (1NH) of siastatin B can also interact with D46, neutralized at pH 4.0. Siastatin B was suggested to have higher affinity to the active pocket of NEU2 than DANA, although it has no C7–9 fragment corresponding to that of DANA. We demonstrated here the pH-dependent affinity of siastatin B toward NEU2 to exhibit potent inhibitory and stabilizing activities. Molecular interaction between siastatin B and NEU2 will be utilized to develop specific inhibitors and stabilizers (chemical chaperones) not only for NEU2 but also the other human sialidases, including NEU1, NEU3 and NEU4, based on homology modeling

Using the fragment molecular orbital method to investigate agonist–orexin-2 receptor interactions

The understanding of binding interactions between any protein and a small molecule plays a key role in the rationalization of affinity and selectivity and is essential for an efficient structure-based drug discovery (SBDD) process. Clearly, to begin SBDD, a structure is needed, and although there has been fantastic progress in solving G-protein-coupled receptor (GPCR) crystal structures, the process remains quite slow and is not currently feasible for every GPCR or GPCR-ligand complex. This situation significantly limits the ability of X-ray crystallography to impact the drug discovery process for GPCR targets in 'real-time' and hence there is still a need for other practical and cost-efficient alternatives. We present here an approach that integrates our previously described hierarchical GPCR modelling protocol (HGMP) and the fragment molecular orbital (FMO) quantum mechanics (QM) method to explore the interactions and selectivity of the human orexin-2 receptor (OX2R) and its recently discovered nonpeptidic agonists. HGMP generates a 3D model of GPCR structures and its complexes with small molecules by applying a set of computational methods. FMO allowsab initioapproaches to be applied to systems that conventional QM methods would find challenging. The key advantage of FMO is that it can reveal information on the individual contribution and chemical nature of each residue and water molecule to the ligand binding that normally would be difficult to detect without QM. We illustrate how the combination of both techniques provides a practical and efficient approach that can be used to analyse the existing structure-function relationships (SAR) and to drive forward SBDD in a real-world example for which there is no crystal structure of the complex available

Osmotic pressure effects identify dehydration upon cytochrome c-cytochrome c oxidase complex formation contributing to a specific electron pathway formation

In the electron transfer (ET) reaction from cytochrome c (Cyt c) to cytochrome c oxidase (CcO), we determined the number and sites of the hydration water released from the protein surface upon the formation of the ET complex by evaluating the osmotic pressure dependence of kinetics for the ET from Cyt c to CcO. We identified that similar to 20 water molecules were dehydrated in complex formation under turnover conditions, and systematic Cyt c mutations in the interaction site for CcO revealed that nearly half of the released hydration water during the complexation were located around Ile81, one of the hydrophobic amino acid residues near the exposed heme periphery of Cyt c. Such a dehydration dominantly compensates for the entropy decrease due to the association of Cyt c with CcO, resulting in the entropy-driven ET reaction. The energetic analysis of the interprotein interactions in the ET complex predicted by the docking simulation suggested the formation of hydrophobic interaction sites surrounding the exposed heme periphery of Cyt c in the Cyt c-CcO interface (a 'molecular breakwater'). Such sites would contribute to the formation of the hydrophobic ET pathway from Cyt c to CcO by blocking water access from the bulk water phase

Energetic Mechanism of Cytochrome c-Cytochrome c Oxidase Electron Transfer Complex Formation under Turnover Conditions Revealed by Mutational Effects and Docking Simulation

Based on the mutational effects on the steady-state kinetics of the electron transfer reaction and our NMR analysis of the interaction site (Sakamoto, K., Kamiya, M., Imai, M., Shinzawa-Itoh, K., Uchida, T., Kawano, K., Yoshikawa, S., and Ishimori, K. (2011) Proc. Natl. Acad. Sci. U.S.A. 108, 1227112276), we determined the structure of the electron transfer complex between cytochrome c (Cyt c) and cytochrome c oxidase (CcO) under turnover conditions and energetically characterized the interactions essential for complex formation. The complex structures predicted by the protein docking simulation were computationally selected and validated by the experimental kinetic data for mutant Cyt c in the electron transfer reaction to CcO. The interaction analysis using the selected Cyt c-CcO complex structure revealed the electrostatic and hydrophobic contributions of each amino acid residue to the free energy required for complex formation. Several charged residues showed large unfavorable (desolvation) electrostatic interactions that were almost cancelled out by large favorable (Columbic) electrostatic interactions but resulted in the destabilization of the complex. The residual destabilizing free energy is compensated by the van der Waals interactions mediated by hydrophobic amino acid residues to give the stabilized complex. Thus, hydrophobic interactions are the primary factors that promote complex formation between Cyt c and CcO under turnover conditions, whereas the change in the electrostatic destabilization free energy provides the variance of the binding free energy in the mutants. The distribution of favorable and unfavorable electrostatic interactions in the interaction site determines the orientation of the binding of Cyt c on CcO

Asking the right questions for mutagenicity prediction from BioMedical text

Abstract Assessing the mutagenicity of chemicals is an essential task in the drug development process. Usually, databases and other structured sources for AMES mutagenicity exist, which have been carefully and laboriously curated from scientific publications. As knowledge accumulates over time, updating these databases is always an overhead and impractical. In this paper, we first propose the problem of predicting the mutagenicity of chemicals from textual information in scientific publications. More simply, given a chemical and evidence in the natural language form from publications where the mutagenicity of the chemical is described, the goal of the model/algorithm is to predict if it is potentially mutagenic or not. For this, we first construct a golden standard data set and then propose MutaPredBERT, a prediction model fine-tuned on B i o L i n k B E R T based on a question-answering formulation of the problem. We leverage transfer learning and use the help of large transformer-based models to achieve a Macro F1 score of >0.88 even with relatively small data for fine-tuning. Our work establishes the utility of large language models for the construction of structured sources of knowledge bases directly from scientific publications