Together, these data suggest that, in order to understand the effect of SNPs in genes of the SLC22 family on drug handling as well as excretion of metabolites like uric acid, it is important to consider the entire set of organic anion transporters. It will be particularly interesting to determine if individuals with nonsynonymous apical and basolateral SNPs have altered handling (and toxicity) of organic anionic drugs and metabolites. Certain OAT family members appear to be under greater evolutionary selection pressure.
Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer library, which may have limitations on their accuracies and usages. In this study, we report an open-source toolkit for protein side-chain modeling, named OPUS-Rota4. It consists of three modules: OPUS-RotaNN2, which predicts protein side-chain dihedral angles; OPUS-RotaCM, which measures the distance and orientation information between the side chain of different residue pairs and OPUS-Fold2, which applies the constraints derived from the first two modules to guide side-chain modeling. OPUS-Rota4 adopts the dihedral angles predicted by OPUS-RotaNN2 as its initial states, and uses OPUS-Fold2 to refine the side-chain conformation with the side-chain contact map constraints derived from OPUS-RotaCM. Therefore, we convert the side-chain modeling problem into a side-chain contact map prediction problem. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to include other differentiable energy terms. OPUS-Rota4 also provides a platform in which the side-chain conformation can be dynamically adjusted under the influence of other processes. We apply OPUS-Rota4 on 15 FM predictions submitted by AlphaFold2 on CASP14, the results show that the side chains modeled by OPUS-Rota4 are closer to their native counterparts than those predicted by AlphaFold2 (e.g. the residue-wise RMSD for all residues and core residues are 0.588 and 0.472 for AlphaFold2, and 0.535 and 0.407 for OPUS-Rota4).
Kidney excretion of numerous organic anionic drugs and endogenous metabolites is carried out by a family of multispecific organic anion transporters (OATs). Two closely related transporters, SLC22A6, initially identified by us as NKT and also known as OAT1, and SLC22A8, also known as OAT3 and ROCT, are thought to mediate the initial steps in the transport of organic anionic drugs between the blood and proximal tubule cells of the kidney. Coding region polymorphisms in these genes are infrequent and pairing of these genes in the genome suggests they may be coordinately regulated. Hence, 5¢ regulatory regions of these genes may be important factors in human variation in organic anionic drug handling. We have analyzed novel single nucleotide polymorphisms in the evolutionarily conserved 5¢ regulatory regions of the SLC22A6 and SLC22A8 genes (phylogenetic footprints) in an ethnically diverse sample of 96 individuals (192 haploid genomes). Only one polymorphism was found in the SLC22A6 5¢ regulatory region. In contrast, seven polymorphisms were found in the SLC22A8 5¢ regulatory region, two of which were common to all ethnic groups studied. Computational analysis permitted phase and haplotype reconstruction. Proximity of these non-coding polymorphisms to transcriptional regulatory elements (including potential sex steroid response elements) suggests a potential influence on the level of transcription of the SLC22A6 and/or SLC22A8 genes and will help define their role in variation in human drug, metabolite and toxin excretion. The clustering of OAT genes in the genome raises the possibility that nucleotide polymorphisms in SLC22A6 could also effect SLC22A8 expression, and vice versa.
Protein backbone torsion angles (Phi and Psi) are crucial for protein local conformation description. In this paper, we propose a general postprocessing method for all prediction methods, namely, OPUS-Refine, which may contribute to the field in a different way. OPUS-Refine is a sampling-based method, therefore, the results of other prediction methods can be used as its constraints. After OPUS-Refine refinement, for instance, the accuracy of Phi/Psi predicted by SPIDER3 and SPOT-1D are both increased. In addition, to facilitate the sampling efficiency, we construct a neighbor-dependent statistical torsion angles sampling database, namely, OPUS-TA, which may be useful for other sampling-based methods. Furthermore, we also introduce the contact map predicted by RaptorX to OPUS-Refine as a global structural constraint. After refinement, compared to the predicted structures obtained from RaptorX online server, the accuracy of both global structural configurations (measured by TM-score and RMSD) and local structural configurations (measured by Phi/Psi) results are improved. OPUS-Refine is a highly efficient framework, it takes only about 4 s to refine the torsion angles and 30 s to refine the global structure of a protein with 100 residues in length on a typical desktop personal computer. Therefore, the sampling-based feature and the efficiency of OPUS-Refine offer greater potentiality for it to take advantage of any other method to achieve better performance.
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