SIMS: A Hybrid Method for Rapid Conformational Analysis

Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their structure. Describing the exact details of these conformational changes, however, remains a central challenge for computational biology due the enormous computational requirements of the problem. This has engendered the development of a rich variety of useful methods designed to answer specific questions at different levels of spatial, temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured Intuitive Move Selector (SIMS), designed to bridge the divide between these two classes, while allowing the benefits of both to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm, borrowed from the field of robotics, in tandem with a well-established protein modeling library. SIMS can combine precise energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate, analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic conformational exploration. We present three example problems that SIMS is applied to and demonstrate a rapid solution for each. These include the automatic determination of ﾑﾑactiveﾒﾒ residues for the hinge-based system Cyanovirin-N, exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose- Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields, demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems

Polymorphisms in the hypoxia-inducible factor 1 alpha gene in Mexican patients with preeclampsia: A case-control study

<p>Abstract</p> <p>Background</p> <p>Although the etiology of preeclampsia is still unclear, recent work suggests that changes in circulating angiogenic factors play a key role in its pathogenesis. In the trophoblast of women with preeclampsia, hypoxia-inducible factor 1 alpha (HIF-1α) is over-expressed, and induces the expression of non-angiogenic factors and inhibitors of trophoblast differentiation. This observation prompted the study of HIF-1α and its relation to preeclampsia. It has been described that the C1772T (P582S) and G1790A (A588T) polymorphisms of the <it>HIF1A </it>gene have significantly greater transcriptional activity, correlated with an increased expression of their proteins, than the wild-type sequence. In this work, we studied whether either or both <it>HIF1A </it>variants contribute to preeclampsia susceptibility.</p> <p>Results</p> <p>Genomic DNA was isolated from 150 preeclamptic and 105 healthy pregnant women. Exon 12 of the <it>HIF1A </it>gene was amplified by PCR, and the genotypes of <it>HIF1A </it>were determined by DNA sequencing.</p> <p>In preeclamptic women and controls, the frequencies of the T allele for C1772T were 4.3 vs. 4.8%, and the frequencies of the A allele for G1790A were 0.0 vs. 0.5%, respectively. No significant differences were found between groups.</p> <p>Conclusion</p> <p>The frequency of the C1772T and G1790A polymorphisms of the <it>HIF1A </it>gene is very low, and neither polymorphism is associated with the development of preeclampsia in the Mexican population.</p

Rapid Sampling of Molecular Motions with Prior Information Constraints

Proteins are active, flexible machines that perform a range of different functions. Innovative experimental approaches may now provide limited partial information about conformational changes along motion pathways of proteins. There is therefore a need for computational approaches that can efficiently incorporate prior information into motion prediction schemes. In this paper, we present PathRover, a general setup designed for the integration of prior information into the motion planning algorithm of rapidly exploring random trees (RRT). Each suggested motion pathway comprises a sequence of low-energy clash-free conformations that satisfy an arbitrary number of prior information constraints. These constraints can be derived from experimental data or from expert intuition about the motion. The incorporation of prior information is very straightforward and significantly narrows down the vast search in the typically high-dimensional conformational space, leading to dramatic reduction in running time. To allow the use of state-of-the-art energy functions and conformational sampling, we have integrated this framework into Rosetta, an accurate protocol for diverse types of structural modeling. The suggested framework can serve as an effective complementary tool for molecular dynamics, Normal Mode Analysis, and other prevalent techniques for predicting motion in proteins. We applied our framework to three different model systems. We show that a limited set of experimentally motivated constraints may effectively bias the simulations toward diverse predicates in an outright fashion, from distance constraints to enforcement of loop closure. In particular, our analysis sheds light on mechanisms of protein domain swapping and on the role of different residues in the motion

Comparative analysis of recurrent neural network architectures for reservoir inflow forecasting

202009 bcmaVersion of RecordPublishe

complications in patients with familial partial lipodystrophy

Objective. Familial partial lipodystrophy (FPLD) is a rare genetic disorder characterized by partial lack of subcutaneous fat.Methods. This multicenter prospective observational study included data from 56 subjects with FPLD (18 independent Turkish families). Thirty healthy controls were enrolled for comparison.Results. Pathogenic variants of the LMNA gene were determined in nine families. Of those, typical exon 8 codon 482 pathogenic variants were identified in four families. Analysis of the LMNA gene also revealed exon 1 codon 47, exon 5 codon 306, exon 6 codon 349, exon 9 codon 528, and exon 11 codon 582 pathogenic variants. Analysis of the PPARG gene revealed exon 3 p.Y151C pathogenic variant in two families and exon 7 p.H477L pathogenic variant in one family. A non-pathogenic exon 5 p.R215Qvariant of the LMNB2 gene was detected in another family. Five other families harbored no mutation in any of the genes sequenced. MRI studies showed slightly different fat distribution patterns among subjects with different point mutations, though it was strikingly different in subjects with LMNA p.R349W pathogenic variant. Subjects with pathogenic variants of the PPARG gene were associated with less prominent fat loss and relatively higher levels of leptin compared to those with pathogenic variants in the LMNA gene. Various metabolic abnormalities associated with insulin resistance were detected in all subjects. End-organ complications were observed.Conclusion. We have identified various pathogenic variants scattered throughout the LMNA and PPARG genes in Turkish patients with FPLD. Phenotypic heterogeneity is remarkable in patients with LMNA pathogenic variants related to the site of missense mutations. FPLD, caused by pathogenic variants either in LMNA or PPARG is associated with metabolic abnormalities associated with insulin resistance that lead to increased morbidity. (C) 2017 Elsevier Inc. All rights reserved