A large number of Cα−H···O contacts are present in transmembrane protein structures, but contribution of such interactions to protein stability is still not well understood. According to previous ab initio quantum calculations, the stabilization energy of a Cα−H···O contact is about 2−3 kcal/mol. However, experimental studies on two different Cα−H···O hydrogen bonds present in transmembrane proteins lead to conclusions that one contact is only weakly stabilizing and the other is not even stabilizing. We note that most previous computational studies were on optimized geometries of isolated molecules, but the experimental measurements were on those in the structural context of transmembrane proteins. In the present study, 263 Cα−H···OC contacts in α-helical transmembrane proteins were extracted from X-ray crystal structures, and interaction energies were calculated with quantum mechanical methods. The average stabilization energy of a Cα−H···OC interaction was computed to be 1.4 kcal/mol. About 13% of contacts were stabilizing by more than 3 kcal/mol, and about 11% were destabilizing. Analysis of the relationships between energy and structure revealed four interaction patterns: three types of attractive cases in which additional Cα−H···O or N−H···O contact is present and a type of repulsive case in which repulsion between two carbonyl oxygen atoms occur. Contribution of Cα−H···OC contacts to protein stability is roughly estimated to be greater than 5 kcal/mol per helix pair for about 16% of transmembrane helices but for only 3% of soluble protein helices. The contribution would be larger if Cα−H···O contacts involving side chain oxygen were also considered.
We participated in CARPI rounds 38-45 both as a server predictor and a human predictor. These CAPRI rounds provided excellent opportunities for testing prediction methods for three classes of protein interactions, that is, protein-protein, protein-peptide, and protein-oligosaccharide interactions. Both template-based methods (GalaxyTBM for monomer protein, GalaxyHomomer for homo-oligomer protein, GalaxyPepDock for protein-peptide complex) and ab initio docking methods (GalaxyTongDock and GalaxyPPDock for protein oligomer, GalaxyPepDock-ab-initio for protein-peptide complex, GalaxyDock2 and Galaxy7TM for protein-oligosaccharide complex) have been tested. Template-based methods depend heavily on the availability of proper templates and template-target similarity, and template-target difference is responsible for inaccuracy of template-based models. Inaccurate template-based models could be improved by our structure refinement and loop modeling methods based on physics-based energy optimization (GalaxyRefineComplex and GalaxyLoop) for several CAPRI targets. Current ab initio docking methods require accurate protein structures as input. Small conformational changes from input structure could be accounted for by our docking methods, producing one of the best models for several CAPRI targets. However, predicting large conformational changes involving protein backbone is still challenging, and full exploration of physics-based methods for such problems is still to come.
Abstract Tailor-made enzymes empower a wide range of versatile applications, although searching for the desirable enzymes often requires high throughput screening and thus poses significant challenges. In this study, we employed homology searches and protein language models to discover and prioritize enzymes by their kinetic parameters. We aimed to discover kynureninases as a potentially versatile therapeutic enzyme, which hydrolyses L-kynurenine, a potent immunosuppressive metabolite, to overcome the immunosuppressive tumor microenvironment in anticancer therapy. Subsequently, we experimentally validated the efficacy of four top-ranked kynureninases under in vitro and in vivo conditions. Our findings revealed a catalytically most active one with a nearly twofold increase in turnover number over the prior best and a 3.4-fold reduction in tumor weight in mouse model comparisons. Consequently, our approach holds promise for the targeted quantitative enzyme discovery and selection suitable for specific applications with higher accuracy, significantly broadening the scope of enzyme utilization. A web-executable version of our workflow is available at seekrank.steineggerlab.com and our code is available as free open-source software at github.com/steineggerlab/SeekRank.
Computational techniques for predicting interactions of proteins and druglike molecules have often been used to search for compounds that bind a given protein with high affinity. More recently, such tools have also been applied to the reverse procedure of searching protein targets for a given compound. Among methods for predicting protein–ligand interactions, ligand-based methods relying on similarity to ligands of known interactions are effective only when similar protein–ligand interactions are known. Receptor-based methods predicting protein–ligand interactions by molecular docking are effective only when high-accuracy receptor structures and binding sites are available. Moreover, the computational cost of molecular docking tends to be too high to be applied to the entire protein structure database. In this paper, an effective target prediction method, which combines ligand similarity-based and receptor structure-based approaches, is introduced. In this method, protein–ligand docking is performed after efficient structure- and similarity-based screening. The enriched protein target database by predicted binding ligands and sites allows detection of protein targets with previously unknown ligand interactions. The method, called GalaxySagittarius, is freely available as a web server at http://galaxy.seoklab.org/sagittarius.
The FALC-Loop web server provides an online interface for protein loop modeling by employing an ab initio loop modeling method called FALC (fragment assembly and analytical loop closure). The server may be used to construct loop regions in homology modeling, to refine unreliable loop regions in experimental structures or to model segments of designed sequences. The FALC method is computationally less expensive than typical ab initio methods because the conformational search space is effectively reduced by the use of fragments derived from a structure database. The analytical loop closure algorithm allows efficient search for loop conformations that fit into the protein framework starting from the fragment-assembled structures. The FALC method shows prediction accuracy comparable to other state-of-the-art loop modeling methods. Top-ranked model structures can be visualized on the web server, and an ensemble of loop structures can be downloaded for further analysis. The web server can be freely accessed at http://falc-loop.seoklab.org/ .
Contemporary template-based modeling techniques allow applications of modeling methods to vast biological problems. However, they tend to fail to provide accurate structures for less-conserved local regions in sequence even when the overall structure can be modeled reliably. We call these regions unreliable local regions (ULRs). Accurate modeling of ULRs is of enormous value because they are frequently involved in functional specificity. In this article, we introduce a new method for modeling ULRs in template-based models by employing a sophisticated loop modeling technique. Combined with our previous study on protein termini, the method is applicable to refinement of both loop and terminus ULRs. A large-scale test carried out in a blind fashion in CASP9 (the 9th Critical Assessment of techniques for protein structure prediction) shows that ULR structures are improved over initial template-based models by refinement in more than 70% of the successfully detected ULRs. It is also notable that successful modeling of several long ULRs over 12 residues is achieved. Overall, the current results show that a careful application of loop and terminus modeling can be a promising tool for model refinement in template-based modeling.
The ordering of silver ions in the superionic conductor $\ensuremath{\alpha}$-AgI has been studied using a discrete version of density-functional theory. We obtained a transition to a low-temperature phase different from that found by Madden et al. using computer simulations [Phys. Rev. B 45, 10 206 (1992)]. We suggest that the difference originates from the fact that the I${}^{\ensuremath{-}}$ lattice is fixed in our study, while the periodic boundary conditions in the simulation allow certain deformations of the I${}^{\ensuremath{-}}$ lattice on the basis of calculated energies for deformed structures. We therefore argue that the structural change of the I${}^{\ensuremath{-}}$ lattice and the ordering of Ag${}^{+}$ ions onto particular sublattices are correlated processes, rather than the ordering of Ag${}^{+}$ ions driving the $\ensuremath{\alpha}--\ensuremath{\beta}$ transition, as suggested by Madden et al. The controversial order-disorder transition within the $\ensuremath{\alpha}$ phase is also discussed in terms of a deformation of the I${}^{\ensuremath{-}}$ lattice.
Abstract Background The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors. Results Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic. Conclusions Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.
The thermostable esterase Est-Y29, belonging to the family VIII lipolytic esterases isolated from metagenomes extracted from the topsoil in Republic of Korea, was identified as a promising catalyst for the production of (S)-ketoprofen, an important nonsteroidal anti-inflammatory drug (NSAID). For industrial applications, the enantioselectivity of the enzyme toward the S-enantiomer of the racemic ketoprofen ester substrate needs to be improved. To understand the structural basis of Est-Y29 enantioselectivity, which is necessary for the rational design of an enzyme with enhanced enantioselectivity, we solved the crystal structures of Est-Y29 bound to (S)-ketoprofen at 1.69 Å resolution. Structural analyses revealed that the S-enantiomer can be stabilized by a π-interaction between the methyl substituent at the chiral carbon of the ligand and the aromatic pocket formed by Tyr123, Phe125, and Tyr170. This finding is further supported by the highly improved enantioselectivity of the mutant Est-Y29 (F125W) toward (S)-ketoprofen due to the enhanced π-interaction. Our results provide the molecular basis of the enantioselectivity of Est-Y29 against (S)-ketoprofen and further offer the opportunity for the rational design of enzyme enantioselectivity as well as potential applications of the mutant Est-Y29 to industrial biocatalysts.
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