Interatomic potentials and solvation parameters from protein engineering data for buried residues

Van der Waals (vdW) interaction energies between different atom types, energies of hydrogen bonds (H‐bonds), and atomic solvation parameters (ASPs) have been derived from the published thermodynamic stabilities of 106 mutants with available crystal structures by use of an originally designed model for the calculation of free‐energy differences. The set of mutants included substitutions of uncharged, inflexible, water‐inaccessible residues in α‐helices and β‐sheets of T4, human, and hen lysozymes and HI ribonuclease. The determined energies of vdW interactions and H‐bonds were smaller than in molecular mechanics and followed the “like dissolves like” rule, as expected in condensed media but not in vacuum. The depths of modified Lennard‐Jones potentials were −0.34, −0.12, and −0.06 kcal/mole for similar atom types (polar–polar, aromatic–aromatic, and aliphatic–aliphatic interactions, respectively) and −0.10, −0.08, −0.06, −0.02, and nearly 0 kcal/mole for different types (sulfur–polar, sulfur–aromatic, sulfur–aliphatic, aliphatic–aromatic, and carbon–polar, respectively), whereas the depths of H‐bond potentials were −1.5 to −1.8 kcal/mole. The obtained solvation parameters, that is, transfer energies from water to the protein interior, were 19, 7, −1, −21, and −66 cal/moleÅ 2 for aliphatic carbon, aromatic carbon, sulfur, nitrogen, and oxygen, respectively, which is close to the cyclohexane scale for aliphatic and aromatic groups but intermediate between octanol and cyclohexane for others. An analysis of additional replacements at the water–protein interface indicates that vdW interactions between protein atoms are reduced when they occur across water.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106915/1/111984_ftp.pd

Variants of the Coagulation and Inflammation Genes Are Replicably Associated with Myocardial Infarction and Epistatically Interact in Russians.

In spite of progress in cardiovascular genetics, data on genetic background of myocardial infarction are still limited and contradictory. This applies as well to the genes involved in inflammation and coagulation processes, which play a crucial role in the disease etiopathogenesis.In this study we found genetic variants of TGFB1, FGB and CRP genes associated with myocardial infarction in discovery and replication groups of Russian descent from the Moscow region and the Republic of Bashkortostan (325/185 and 220/197 samples, correspondingly). We also found and replicated biallelic combinations of TGFB1 with FGB, TGFB1 with CRP and IFNG with PTGS1 genetic variants associated with myocardial infarction providing a detectable cumulative effect. We proposed an original two-component procedure for the analysis of nonlinear (epistatic) interactions between the genes in biallelic combinations and confirmed the epistasis hypothesis for the set of alleles of IFNG with PTGS. The procedure is applicable to any pair of logical variables, e.g. carriage of two sets of alleles. The composite model that included three single gene variants and the epistatic pair has AUC of 0.66 both in discovery and replication groups.The genetic impact of TGFB1, FGB, CRP, IFNG, and PTGS and/or their biallelic combinations on myocardial infarction was found and replicated in Russians. Evidence of epistatic interactions between IFNG with PTGS genes was obtained both in discovery and replication groups

The map of possible interactions between components of MI-associated biallelic combination <i>IFNG</i> and <i>PTGS1</i> (black circles) and ten relative partners (gray circles) generated by GeneMania online software [45].

<p>Possible physical interactions (pink), co-expression (violet), pathway (blue), genetic interactions (green), and shared protein domains (yellow) are shown. IDO1 –indoleamine 2,3–dioxygenase 1; IFNG–interferon gamma; IFNGR1 –interferon gamma receptor 1; IFNGR2 –interferon gamma receptor 2; IRF1 –interferon regulatory factor 1; MPO–myeloperoxidase; PTGIS–prostaglandin I2 (prostacyclin) synthase; PRKCD–protein kinase C delta; PTGS1 –prostaglandin–endoperoxide synthase 1; PTGS2 –prostaglandin–endoperoxide synthase 2; PTPN2 –protein tyrosine phosphatase, non–receptor type 2; PTPN6 –protein tyrosine phosphatase, non–receptor type 6.</p

ROC curves demonstrate usefulness of the additive composite model built from all identified genetic markers.

<p><b>A</b>. Comparing performance of the composite model to the performance of each single marker in the Moscow discovery sample. Combining the high specificity of <i>CRP</i> and <i>IFNG</i>+<i>PTGS</i> predictors (the left hump) with relatively high sensitivity of <i>TGFB1</i> and <i>FGB</i> (the right hump) yields a much better classifier. <b>B</b>. Performance of the model stays the same when tested on the independent replication sample (Bashkortostan).</p

Entangled Proteins: Knots, Slipknots, Links, and Lassos

In recent years the studies of entangled proteins have grown into the whole new, interdisciplinary and rapidly developing field of research. Here we present various types of entangled proteins studied within this field, which form knots, slipknots, links, and lassos. We discuss their geometric features and indicate what biological and physical role the entanglement plays. We also discuss mathematical tools necessary to analyze such structures and present databases and servers assembling information about entangled proteins: KnotProt, LinkProt, and LassoProt