Characterizing the structural variability of HIV-2 protease upon the binding of diverse ligands using a structural alphabet approach

Abstract

The HIV-2 protease (PR2) is an important target for designing new drugs against the HIV-2 infection. In this study, we explored the structural backbone variability of all available PR2 structures complexed with various inhibitors using a structural alphabet approach. 77% of PR2 positions are structurally variable, meaning they exhibit different local conformations in PR2 structures. This variability was observed all along the structure, particularly in the elbow and flap regions. A part of these backbone changes observed between the 18 PR2 is induced by intrinsic flexibility, and ligand binding putatively induces others occurring in the binding pocket. These latter changes could be important for PR2 adaptation to diverse ligands and are accompanied by changes outside the binding pocket. In addition, the study of the link between structural variability of the pocket and PR2–ligand interactions allowed us to localize pocket regions important for ligand binding and catalytic function, regions important for ligand recognition that adjust their backbone in response to ligand binding and regions important for the pocket opening and closing that have large intrinsic flexibility. Finally, we suggested that differences in ligand effectiveness for PR2 could be partially explained by different backbone deformations induced by these ligands. To conclude, this study is the first characterization of the PR2 structural variability considering ligand diversity. It provides information about the recognition of PR2 to various ligands and its mechanisms to adapt its local conformation to bound ligands that could help understand the resistance of PR2 to its inhibitors, a major antiretroviral class.

Communicated by Ramaswamy H. Sarma     

Sequence alignments and pair hidden Markov models using evolutionary history

This work presents a novel pairwise statistical alignment method based on an explicit evolutionary model of insertions and deletions (indels). Indel events of any length are possible according to a geometric distribution. The geometric distribution parameter, the indel rate, and the evolutionary time are all maximum likelihood estimated from the sequences being aligned. Probability calculations are done using a pair hidden Markov model (HMM) with transition probabilities calculated from the indel parameters. Equations for the transition probabilities make the pair HMM closely approximate the specified indel model. The method provides an optimal alignment, its likelihood, the likelihood of all possible alignments, and the reliability of individual alignment regions. Human alpha and beta-hemoglobin sequences are aligned, as an illustration of the potential utility of this pair HMM approach.     

基于隐马氏模型对编码序列缺失与插入的检测(英)

在基因组测序工作完成后,利用计算工具进行基因识别以及基因结构预测受到了越来越多人的重视.人们开发了大量的相关应用软件,如GenScan, Genemark, GRAIL等,这些软件在寻找新基因方面提供了很重要的线索.但基因的识别和预测问题仍未得到完全解决,当目标基因的编码序列有缺失和插入时,其预测结果和基因的实际结构相差很大.为了消除测序错误对预测结果的影响,希望能找出编码序列区的测序错误.基于这种想法,尝试根据DNA序列的一些统计特性,利用隐马尔科夫模型（Hidden Markov Model）,引入缺失和插入状态,然后用Viterbi算法,从中找出含有缺失和插入的外显子序列片段.在常用的Burset/Guigo检测集进行检测,得到的结果在外显子水平上,Sn(sensitivity)和Sp(specificity)均达到84%以上.     

HumGene:一个基于GHMM的人类基因预测软件

HumGene是一个采用广义隐Markov模型(GHMM)的人类基因预测软件.利用人类基因的结构特点,采用概率模型为基因结构中各个特定区域建立了独立的子模型,能够获得全局统一的评价指数,使得系统整体框架具有一定的扩展性.采用一种新的简化算法,有效地降低了计算的复杂度.介绍了软件的构成,对软件进行了测试,给出了与其它类似软件的结果比较.     

Estimating Variable Effective Population Sizes from Multiple Genomes: A Sequentially Markov Conditional Sampling Distribution Approach

Throughout history, the population size of modern humans has varied considerably due to changes in environment, culture, and technology. More accurate estimates of population size changes, and when they occurred, should provide a clearer picture of human colonization history and help remove confounding effects from natural selection inference. Demography influences the pattern of genetic variation in a population, and thus genomic data of multiple individuals sampled from one or more present-day populations contain valuable information about the past demographic history. Recently, Li and Durbin developed a coalescent-based hidden Markov model, called the pairwise sequentially Markovian coalescent (PSMC), for a pair of chromosomes (or one diploid individual) to estimate past population sizes. This is an efficient, useful approach, but its accuracy in the very recent past is hampered by the fact that, because of the small sample size, only few coalescence events occur in that period. Multiple genomes from the same population contain more information about the recent past, but are also more computationally challenging to study jointly in a coalescent framework. Here, we present a new coalescent-based method that can efficiently infer population size changes from multiple genomes, providing access to a new store of information about the recent past. Our work generalizes the recently developed sequentially Markov conditional sampling distribution framework, which provides an accurate approximation of the probability of observing a newly sampled haplotype given a set of previously sampled haplotypes. Simulation results demonstrate that we can accurately reconstruct the true population histories, with a significant improvement over the PSMC in the recent past. We apply our method, called diCal, to the genomes of multiple human individuals of European and African ancestry to obtain a detailed population size change history during recent times.     

Sequence and structural determinants of strand swapping in cadherin domains: do all cadherins bind through the same adhesive interface?

Cadherins are cell surface adhesion proteins important for tissue development and integrity. Type I and type II, or classical, cadherins form adhesive dimers via an interface formed through the exchange, or “swapping”, of the N-terminal β-strands from their membrane-distal EC1 domains. Here, we ask which sequence and structural features in EC1 domains are responsible for β-strand swapping and whether members of other cadherin families form similar strand-swapped binding interfaces. We created a comprehensive database of multiple alignments of each type of cadherin domain. We used the known three-dimensional structures of classical cadherins to identify conserved positions in multiple sequence alignments that appear to be crucial determinants of the cadherin domain structure. We identified features that are unique to EC1 domains. On the basis of our analysis, we conclude that all cadherin domains have very similar overall folds but, with the exception of classical and desmosomal cadherin EC1 domains, most of them do not appear to bind through a strand-swapping mechanism. Thus, non-classical cadherins that function in adhesion are likely to use different protein-protein interaction interfaces. Our results have implications for the evolution of molecular mechanisms of cadherin-mediated adhesion in vertebrates.     

The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)

A mixed hidden Markov model (HMM) was developed for predicting breeding values of a biomarker (here, somatic cell score) and the individual probabilities of health and disease (here, mastitis) based upon the measurements of the biomarker. At a first level, the unobserved disease process (Markov model) was introduced and at a second level, the measurement process was modeled, making the link between the unobserved disease states and the observed biomarker values. This hierarchical formulation allows joint estimation of the parameters of both processes. The flexibility of this approach is illustrated on the simulated data. Firstly, lactation curves for the biomarker were generated based upon published parameters (mean, variance, and probabilities of infection) for cows with known clinical conditions (health or mastitis due to Escherichia coli or Staphylococcus aureus). Next, estimation of the parameters was performed via Gibbs sampling, assuming the health status was unknown. Results from the simulations and mathematics show that the mixed HMM is appropriate to estimate the quantities of interest although the accuracy of the estimates is moderate when the prevalence of the disease is low. The paper ends with some indications for further developments of the methodology.