Knowledge-based energy functions for computational studies of proteins

This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe

Genome-wide association study for systemic lupus erythematosus in an egyptian population

Systemic lupus erythematosus (SLE) susceptibility has a strong genetic component. Genome-wide association studies (GWAS) across trans-ancestral populations show both common and distinct genetic variants of susceptibility across European and Asian ancestries, while many other ethnic populations remain underexplored. We conducted the first SLE GWAS on Egyptians–an admixed North African/Middle Eastern population–using 537 patients and 883 controls. To identify novel susceptibility loci and replicate previously known loci, we performed imputation-based association analysis with 6,382,276 SNPs while accounting for individual admixture. We validated the association analysis using adaptive permutation tests (n = 109). We identified a novel genome-wide significant locus near IRS1/miR-5702 (Pcorrected = 1.98 × 10−8) and eight novel suggestive loci (Pcorrected 0.8) with lead SNPs from four suggestive loci (ARMC9, DIAPH3, IFLDT1, and ENTPD3) were associated with differential gene expression (3.5 × 10−95 < p < 1.0 × 10−2) across diverse tissues. These loci are involved in cellular proliferation and invasion—pathways prominent in lupus and nephritis. Our study highlights the utility of GWAS in an admixed Egyptian population for delineating new genetic associations and for understanding SLE pathogenesis

Cofactor engineering of Lactobacillus brevis alcohol dehydrogenase by computational design

The R-specific alcohol dehydrogenase from Lactobacillus brevis (Lb-ADH) catalyzes the enantioselective reduction of prochiral ketones to the corresponding secondary alcohols. It is stable and has broad substrate specificity. These features make this enzyme an attractive candidate for biotechnological applications. A drawback is its preference for NADP(H) as a cofactor, which is more expensive and labile than NAD(H). Structure-based computational protein engineering was used to predict mutations to alter the cofactor specificity of Lb-ADH. Mutations were introduced into Lb-ADH and tested against the substrate acetophenone, with either NAD(H) or NADP(H) as cofactor. The mutant Arg38Pro showed fourfold increased activity with acetophenone and NAD(H) relative to the wild type. Both Arg38Pro and wild type exhibit a pH optimum of 5.5 with NAD(H) as cofactor, significantly more acidic than with NADP(H). These and related Lb-ADH mutants may prove useful for the green synthesis of pharmaceutical precursor

A Cre-dependent GCaMP3 reporter mouse for neuronal imaging in vivo

Fluorescent calcium indicator proteins, such as GCaMP3, allow imaging of activity in genetically defined neuronal populations. GCaMP3 can be expressed using various gene delivery methods, such as viral infection or electroporation. However, these methods are invasive and provide inhomogeneous and nonstationary expression. Here, we developed a genetic reporter mouse, Ai38, which expressesGCaMP3 in a Cre-dependent manner from the ROSA26 locus, driven by a strong CAG promoter. Crossing Ai38 with appropriate Cre mice produced robust GCaMP3 expression in defined cell populations in the retina, cortex, and cerebellum. In the primary visual cortex, visually evoked GCaMP3 signals showed normal orientatio

A Bayesian approach for determining protein side-chain rotamer conformations using unassigned NOE data

Abstract. A major bottleneck in protein structure determination via nuclear magnetic resonance (NMR) is the lengthy and laborious process of assigning resonances and nuclear Overhauser effect (NOE) cross peaks. Recent studies have shown that accurate backbone folds can be determined using sparse NMR data, such as residual dipolar couplings (RDCs) or backbone chemical shifts. This opens a question of whether we can also determine the accurate protein sidechain conformations using sparse or unassigned NMR data. We attack this question by using unassigned nuclear Overhauser effect spectroscopy (NOESY) data, which record the through-space dipolar interactions between protons nearby in 3D space. We propose a Bayesian approach with a Markov random field (MRF) model to integrate the likelihood function derived from observed experimental data, with prior information (i.e., empirical molecular mechanics energies) about the protein structures. We unify the side-chain structure prediction problem with the side-chain structure determination problem using unassigned NMR data, and apply the deterministic dead-end elimination (DEE) and A * search algorithms t