Rapid sampling of local minima in protein energy surface and effective reduction through a multi-objective filter

Background

Many problems in protein modeling require obtaining a discrete representation of the protein conformational space as an ensemble of conformations. In ab-initio structure prediction, in particular, where the goal is to predict the native structure of a protein chain given its amino-acid sequence, the ensemble needs to satisfy energetic constraints. Given the thermodynamic hypothesis, an effective ensemble contains low-energy conformations which are similar to the native structure. The high-dimensionality of the conformational space and the ruggedness of the underlying energy surface currently make it very difficult to obtain such an ensemble. Recent studies have proposed that Basin Hopping is a promising probabilistic search framework to obtain a discrete representation of the protein energy surface in terms of local minima. Basin Hopping performs a series of structural perturbations followed by energy minimizations with the goal of hopping between nearby energy minima. This approach has been shown to be effective in obtaining conformations near the native structure for small systems. Recent work by us has extended this framework to larger systems through employment of the molecular fragment replacement technique, resulting in rapid sampling of large ensembles.

Methods

This paper investigates the algorithmic components in Basin Hopping to both understand and control their effect on the sampling of near-native minima. Realizing that such an ensemble is reduced before further refinement in full ab-initio protocols, we take an additional step and analyze the quality of the ensemble retained by ensemble reduction techniques. We propose a novel multi-objective technique based on the Pareto front to filter the ensemble of sampled local minima.

Results and conclusions

We show that controlling the magnitude of the perturbation allows directly controlling the distance between consecutively-sampled local minima and, in turn, steering the exploration towards conformations near the native structure. For the minimization step, we show that the addition of Metropolis Monte Carlo-based minimization is no more effective than a simple greedy search. Finally, we show that the size of the ensemble of sampled local minima can be effectively and efficiently reduced by a multi-objective filter to obtain a simpler representation of the probed energy surface.

     

Generation of mice expressing only the long form of the prolactin receptor reveals that both isoforms of the receptor are required for normal ovarian function

Prolactin (PRL), a pleiotropic hormone essential for maintenance of corpus luteum (CL) function and pregnancy, transduces its signal through two types of receptors, a short form (PRLR-S) and a long form (PRLR-L). Both types of receptors are expressed in the CL, yet their individual roles are not well defined. We have shown previously that female transgenic mice expressing only PRLR-S display total infertility characterized by defective follicular development and early degeneration of CL, suggesting that expression of PRLR-L is a prerequisite for normal follicular development and maintenance of CL. To determine whether PRLR-L alone is the sole receptor required to maintain normal CL formation, differentiation, and progesterone secretion, we generated two transgenic mice which express only PRLR-L, either ubiquitously (Tg-RL) or in a CL-specific manner (CL-RL). To generate CL-specific expression, we used the HSD17B7 promoter. We found both transgenic mice models cycled normally, displayed no apparent defect in follicular development, and had normal ovulation rates. The STAT5 signaling pathway, considered essential for luteinization and progesterone production, was activated by PRL in both transgenic mice models. However, soon after mating, Tg-RL and CL-RL mice showed early regression of CL, lack of progesterone production, and implantation failure that rendered them totally infertile. Embryo transfer studies demonstrated no embryo abnormalities, and supplementation with progesterone rescued implantation failure in these mice. Close observation revealed lack of luteinization and reduced expression of proteins involved in progesterone biosynthesis despite normal levels of LHCGR (LH-R), ESR1 (ER-alpha), CEBPB (C/EBP-beta) and CDKN1B (p27), proteins essential for luteinization. However, we found VEGFA, a key regulator of angiogenesis and vascularization, to be dramatically reduced in both Tg-RL and CL-RL mice. We also found collagen IV, a marker for the basal lamina of endothelial cells, aberrantly expressed and a discordant organization of endothelial cells in CL. Although luteinization did not occur in vivo, granulosa cells isolated from these mice luteinized in culture. Taken together, these results suggest that a vascularization defect in the CL may be responsible for lack of luteinization, progesterone production, and infertility in mice expressing only PRLR-L. This investigation therefore demonstrates that in contrast to earlier presumptions that PRLR-L alone is able to support normal CL formation and function, both isoforms of the PRL receptor are required in the CL for normal female fertility.     

From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes

Background

The protein energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. Reconstructing a protein’s energy landscape holds the key to characterizing a protein’s equilibrium conformational dynamics and its relationship to function. Many pathogenic mutations in protein sequences alter the equilibrium dynamics that regulates molecular interactions and thus protein function. In principle, reconstructing energy landscapes of a protein’s healthy and diseased variants is a central step to understanding how mutations impact dynamics, biological mechanisms, and function.

Results

Recent computational advances are yielding detailed, sample-based representations of protein energy landscapes. In this paper, we propose and describe two novel methods that leverage computed, sample-based representations of landscapes to reconstruct them and extract from them informative local structures that reveal the underlying organization of an energy landscape. Such structures constitute landscape features that, as we demonstrate here, can be utilized to detect alterations of landscapes upon mutation.

Conclusions

The proposed methods detect altered protein energy landscape features in response to sequence mutations. By doing so, the methods allow formulating hypotheses on the impact of mutations on specific biological activities of a protein. This work demonstrates that the availability of energy landscapes of healthy and diseased variants of a protein opens up new avenues to harness the quantitative information embedded in landscapes to summarize mechanisms via which mutations alter protein dynamics to percolate to dysfunction.

     

Diversity and phylogenetic relationships of <Emphasis Type="Italic">Glossina</Emphasis> populations in Nigeria and the Cameroonian border region

Background

Tsetse flies are vectors of trypanosomes, parasites that cause devastating disease in humans and livestock. In the course of vector control programmes it is necessary to know about the Glossina species present in the study area, the population dynamics and the genetic exchange between tsetse fly populations.

Results

To achieve an overview of the tsetse fly diversity in Nigeria and at the Nigeria-Cameroon border, tsetse flies were trapped and collected between February and March 2014 and December 2016. Species diversity was determined morphologically and by analysis of Cytochrome C Oxidase SU1 (COI) gene sequences. Internal transcribed spacer-1 (ITS-1) sequences were compared to analyse variations within populations. The most dominant species were G. m. submorsitans, G. tachinoides and G. p. palpalis. In Yankari Game Reserve and Kainji Lake National Park, G. submorsitans and G. tachinoides were most frequent, whereas in Old Oyo National Park and Ijah Gwari G. p. palpalis was the dominant species. Interestingly, four unidentified species were recorded during the survey, for which no information on COI or ITS-1 sequences exists. G. p. palpalis populations showed a segregation in two clusters along the Cameroon-Nigerian border.

Conclusions

The improved understanding of the tsetse populations in Nigeria will support decisions on the scale in which vector control is likely to be more effective. In order to understand in more detail how isolated these populations are, it is recommended that further studies on gene flow be carried out using other markers, including microsatellites.

     

Inhibition of MAPK by prolactin signaling through the short form of its receptor in the ovary and decidua: involvement of a novel phosphatase

Prolactin (PRL) is essential for normal reproduction and signals through two types of receptors, the short (PRL-RS) and long (PRL-RL) form. We have previously shown that transgenic mice expressing only PRL-RS (PRLR(-/-)RS) display abnormal follicular development and premature ovarian failure. Here, we report that MAPK, essential for normal follicular development, is critically inhibited by PRL in reproductive tissues of PRLR(-/-)RS mice. Consequently, the phosphorylation of MAPK downstream targets are also markedly inhibited by PRL without affecting immediate upstream kinases, suggesting involvement of MAPK specific phosphatase(s) in this inhibition. Similar results are obtained in a PRL-responsive ovary-derived cell line (GG-CL) that expresses only PRL-RS. However, we found the expression/activation of several known MAPK phosphatases not to be affected by PRL, suggesting a role of unidentified phosphatase(s). We detected a 27-kDa protein that binds to the intracellular domain of PRL-RS and identified it as dual specific phosphatase DUPD1. PRL does not induce expression of DUDP1 but represses its phosphorylation on Thr-155. We also show a physical association of this phosphatase with ERK1/2 and p38 MAPK. Using an in vitro phosphatase assay and overexpression studies, we established that DUPD1 is a MAPK phosphatase. Dual specific phosphatase inhibitors as well as siRNA to DUPD1, completely prevent PRL-mediated MAPK inhibition in ovarian cells. Our results strongly suggest that deactivation of MAPK by PRL/PRL-RS contributes to the severe ovarian defect in PRLR(-/-)RS mice and demonstrate the novel association of PRL-RS with DUPD1 and a role for this phosphatase in MAPK deactivation.     

A two-stage evolutionary approach for effective classification of hypersensitive DNA sequences

Hypersensitive (HS) sites in genomic sequences are reliable markers of DNA regulatory regions that control gene expression. Annotation of regulatory regions is important in understanding phenotypical differences among cells and diseases linked to pathologies in protein expression. Several computational techniques are devoted to mapping out regulatory regions in DNA by initially identifying HS sequences. Statistical learning techniques like Support Vector Machines (SVM), for instance, are employed to classify DNA sequences as HS or non-HS. This paper proposes a method to automate the basic steps in designing an SVM that improves the accuracy of such classification. The method proceeds in two stages and makes use of evolutionary algorithms. An evolutionary algorithm first designs optimal sequence motifs to associate explicit discriminating feature vectors with input DNA sequences. A second evolutionary algorithm then designs SVM kernel functions and parameters that optimally separate the HS and non-HS classes. Results show that this two-stage method significantly improves SVM classification accuracy. The method promises to be generally useful in automating the analysis of biological sequences, and we post its source code on our website.     

An evolutionary conservation-based method for refining and reranking protein complex structures

Detection of protein complexes and their structures is crucial for understanding their role in the basic biology of organisms. Computational docking methods can provide researchers with a good starting point for the analysis of protein complexes. However, these methods are often not accurate and their results need to be further refined to improve interface packing. In this paper, we introduce a refinement method that incorporates evolutionary information into a novel scoring function by employing Evolutionary Trace (ET)-based scores. Our method also takes Van der Waals interactions into account to avoid atomic clashes in refined structures. We tested our method on docked candidates of eight protein complexes and the results suggest that the proposed scoring function helps bias the search toward complexes with native interactions. We show a strong correlation between evolutionary-conserved residues and correct interface packing. Our refinement method is able to produce structures with better lRMSD (least RMSD) with respect to the known complexes and lower energies than initial docked structures. It also helps to filter out false-positive complexes generated by docking methods, by detecting little or no conserved residues on false interfaces. We believe this method is a step toward better ranking and prediction of protein complexes.     

Recruitment of the nucleotide excision repair endonuclease XPG to sites of UV-induced dna damage depends on functional TFIIH

The structure-specific endonuclease XPG is an indispensable core protein of the nucleotide excision repair (NER) machinery. XPG cleaves the DNA strand at the 3' side of the DNA damage. XPG binding stabilizes the NER preincision complex and is essential for the 5' incision by the ERCC1/XPF endonuclease. We have studied the dynamic role of XPG in its different cellular functions in living cells. We have created mammalian cell lines that lack functional endogenous XPG and stably express enhanced green fluorescent protein (eGFP)-tagged XPG. Life cell imaging shows that in undamaged cells XPG-eGFP is uniformly distributed throughout the cell nucleus, diffuses freely, and is not stably associated with other nuclear proteins. XPG is recruited to UV-damaged DNA with a half-life of 200 s and is bound for 4 min in NER complexes. Recruitment requires functional TFIIH, although some TFIIH mutants allow slow XPG recruitment. Remarkably, binding of XPG to damaged DNA does not require the DDB2 protein, which is thought to enhance damage recognition by NER factor XPC. Together, our data present a comprehensive view of the in vivo behavior of a protein that is involved in a complex chromatin-associated process.     

HopDock: a probabilistic search algorithm for decoy sampling in protein-protein docking

Background

Elucidating the three-dimensional structure of a higher-order molecular assembly formed by interacting molecular units, a problem commonly known as docking, is central to unraveling the molecular basis of cellular activities. Though protein assemblies are ubiquitous in the cell, it is currently challenging to predict the native structure of a protein assembly in silico.

Methods

This work proposes HopDock, a novel search algorithm for protein-protein docking. HopDock efficiently obtains an ensemble of low-energy dimeric configurations, also known as decoys, that can be effectively used by ab-initio docking protocols. HopDock is based on the Basin Hopping (BH) framework which perturbs the structure of a dimeric configuration and then follows it up with an energy minimization to explicitly sample a local minimum of a chosen energy function. This process is repeated in order to sample consecutive energy minima in a trajectory-like fashion. HopDock employs both geometry and evolutionary conservation analysis to narrow down the interaction search space of interest for the purpose of efficiently obtaining a diverse decoy ensemble.

Results and conclusions

A detailed analysis and a comparative study on seventeen different dimers shows HopDock obtains a broad view of the energy surface near the native dimeric structure and samples many near-native configurations. The results show that HopDock has high sampling capability and can be employed to effectively obtain a large and diverse ensemble of decoy configurations that can then be further refined in greater structural detail in ab-initio docking protocols.