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1. Biophysical characterization of the CXC chemokine receptor 2 ligands

   https://doi.org/10.1371/journal.pone.0298418  

   Martin, Patrick; Kurth, Emily A; Budean, David; Momplaisir, Nathalie; Qu, Elaine; Simien, Jennifer M; Orellana, Grace E; Brautigam, Chad A; Smrcka, Alan V; Haglund, Ellinor (April 2024, PLOS ONE)

   Jeyaseelan, Samithamby (Ed.)

   The chemokines of the immune system act as first responders by operating as chemoattractants, directing immune cells to specific locations of inflamed tissues. This promiscuous network is comprised of 50 ligands and 18 receptors where the ligands may interact with the receptors in various oligomeric states i.e., monomers, homodimers, and heterodimers. Chemokine receptors are G-protein coupled receptors (GPCRs) present in the membrane of immune cells. The migration of immune cells occurs in response to a concentration gradient of the ligands. Chemotaxis of neutrophils is directed by CXC-ligand (CXCL) activation of the membrane bound CXC chemokine receptor 2 (CXCR2). CXCR2 plays an important role in human health and is linked to disorders such as autoimmune disorders, inflammation, and cancer. Yet, despite their important role, little is known about the biophysical characteristics controlling ligand:ligand and ligand:receptor interaction essential for biological activity. In this work, we study the homodimers of three of the CXCR2 cognate ligands, CXCL1, CXCL5, and CXCL8. The ligands share high structural integrity but a low sequence identity. We show that the sequence diversity has evolved different binding affinities and stabilities for the CXC-ligands resulting in diverse agonist/antagonist behavior. Furthermore, CXC-ligands fold through a three-state mechanism, populating a folded monomeric state before associating into an active dimer. 

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2. A quantitative assessment of deformation energy in intermolecular interactions: How important is it?

   https://doi.org/10.1063/5.0155895  

   Sargent, Caroline T.; Kasera, Raina; Glick, Zachary L.; Sherrill, C. David; Cheney, Daniel L. (June 2023, The Journal of Chemical Physics)

   Dimer interaction energies have been well studied in computational chemistry, but they can offer an incomplete understanding of molecular binding depending on the system. In the current study, we present a dataset of focal-point coupled-cluster interaction and deformation energies (summing to binding energies, De) of 28 organic molecular dimers. We use these highly accurate energies to evaluate ten density functional approximations for their accuracy. The best performing method (with a double-ζ basis set), B97M-D3BJ, is then used to calculate the binding energies of 104 organic dimers, and we analyze the influence of the nature and strength of interaction on deformation energies. Deformation energies can be as large as 50% of the dimer interaction energy, especially when hydrogen bonding is present. In most cases, two or more hydrogen bonds present in a dimer correspond to an interaction energy of −10 to −25 kcal mol−1, allowing a deformation energy above 1 kcal mol−1 (and up to 9.5 kcal mol−1). A lack of hydrogen bonding usually restricts the deformation energy to below 1 kcal mol−1 due to the weaker interaction energy. 

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3. PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features

   https://doi.org/10.1016/j.ymeth.2023.03.002  

   Harini, K.; Kihara, Daisuke; Michael Gromiha, M. (May 2023, Methods)

   Protein–DNA interactions play an important role in various biological processes such as gene expression, replication, and transcription. Understanding the important features that dictate the binding affinity of protein-DNA complexes and predicting their affinities is important for elucidating their recognition mechanisms. In this work, we have collected the experimental binding free energy (ΔG) for a set of 391 Protein-DNA complexes and derived several structure-based features such as interaction energy, contact potentials, volume and surface area of binding site residues, base step parameters of the DNA and contacts between different types of atoms. Our analysis on relationship between binding affinity and structural features revealed that the important factors mainly depend on the number of DNA strands as well as functional and structural classes of proteins. Specifically, binding site properties such as number of atom contacts between the DNA and protein, volume of protein binding sites and interaction-based features such as interaction energies and contact potentials are important to understand the binding affinity. Further, we developed multiple regression equations for predicting the binding affinity of protein-DNA complexes belonging to different structural and functional classes. Our method showed an average correlation and mean absolute error of 0.78 and 0.98 kcal/mol, respectively, between the experimental and predicted binding affinities on a jack-knife test. We have developed a webserver, PDA-PreD (Protein-DNA Binding affinity predictor), for predicting the affinity of protein-DNA complexes and it is freely available at https://web.iitm.ac.in/bioinfo2/pdapred/ 

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4. Enhancement of the Tetrel Bond by the Effects of Substituents, Cooperativity, and Electric Field: Transition from Noncovalent to Covalent Bond

   https://doi.org/10.1002/cphc.202100612  

   Liu, Na; Xie, Xiaoying; Li, Qingzhong; Scheiner, Steve (September 2021, ChemPhysChem)

   Abstract The T⋅⋅⋅N tetrel bond (TB) formed between TX₃OH (T=C, Si, Ge; X=H, F) and the Lewis base N≡CM (M=H, Li, Na) is studied by ab initio calculations at the MP2/aug‐cc‐pVTZ level. Complexes involving TH₃OH contain a conventional TB with interaction energy less than 10 kcal/mol. This bond is substantially strengthened, approaching 35 kcal/mol and covalent character, when fluorosubstituted TF₃OH is combined with NCLi or NCNa. Along with this enhanced binding comes a near equalization of the TB T⋅⋅⋅N and the internal T−O bond lengths, and the associated structure acquires a trigonal bipyramidal shape, despite a high internal deformation energy. This structural transformation becomes more complete, and the TB is further strengthened upon adding an electron acceptor BeCl₂to the Lewis acid and a base to the NCM unit. This same TB strengthening can be accomplished also by imposition of an external electric field. 

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5. SSIPe: accurately estimating protein–protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function

   https://doi.org/10.1093/bioinformatics/btz926  

   Huang, Xiaoqiang; Zheng, Wei; Pearce, Robin; Zhang, Yang; Valencia, ed., Alfonso (December 2019, Bioinformatics)

   Abstract MotivationMost proteins perform their biological functions through interactions with other proteins in cells. Amino acid mutations, especially those occurring at protein interfaces, can change the stability of protein–protein interactions (PPIs) and impact their functions, which may cause various human diseases. Quantitative estimation of the binding affinity changes (ΔΔGbind) caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. ResultsWe present SSIPe, which combines protein interface profiles, collected from structural and sequence homology searches, with a physics-based energy function for accurate ΔΔGbind estimation. To offset the statistical limits of the PPI structure and sequence databases, amino acid-specific pseudocounts were introduced to enhance the profile accuracy. SSIPe was evaluated on large-scale experimental data containing 2204 mutations from 177 proteins, where training and test datasets were stringently separated with the sequence identity between proteins from the two datasets below 30%. The Pearson correlation coefficient between estimated and experimental ΔΔGbind was 0.61 with a root-mean-square-error of 1.93 kcal/mol, which was significantly better than the other methods. Detailed data analyses revealed that the major advantage of SSIPe over other traditional approaches lies in the novel combination of the physical energy function with the new knowledge-based interface profile. SSIPe also considerably outperformed a former profile-based method (BindProfX) due to the newly introduced sequence profiles and optimized pseudocount technique that allows for consideration of amino acid-specific prior mutation probabilities. Availability and implementationWeb-server/standalone program, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/SSIPe and https://github.com/tommyhuangthu/SSIPe. Supplementary informationSupplementary data are available at Bioinformatics online. 

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