Evolutionary coupling methods in de novo protein structure prediction

An understanding of protein tertiary structure is important for both basic and translational research, for example to understand molecular mechanisms, engineer new or optimized catalysts, or formulate new cures. Protein tertiary structures are typically determined experimentally, a time-consuming process with average costs in the hundred thousands of US dollars for determining a single protein structure. Consequently, there is much interest in using computational methods for driving down the cost of obtaining new structures. While great successes have been made in transferring structural information from already structurally solved homologous proteins, the sensitivity improvements of methods for detecting homologous proteins have plateaued in recent years and homology-based Protein structure prediction is ultimately limited by the availability of a suitable template that must be determined experimentally. De novo protein structure prediction could theoretically use physical models to determine the native conformation of a protein without Prior structural information but in practice, such approaches are limited by the computational costs of evaluating expensive energy functions for many different points in an enormous search space. An old idea in protein bioinformatics is to use the compensatory mutations observed due to the evolutionary pressure of maintaining a protein fold to predict which residue pairs in a protein structures are interacting in the folded structure. If such interactions can be reliably predicted, they can be used to constrain the search space of de novo protein structure prediction sufficiently so that the lowest-energy conformation can be found. Through recent improvements in the accuracy of such residue-residue interaction predictors, Protein domain structures of typical size could be predicted in a blinded experiment for the first time in 2011. However, the new class of methods is still limited in its applicability in that methods are sensitive to false-positive predictions of interactions and can only provide reliable predictions with low false-positive rates for Protein families that have a high number of homologous sequences. This work aims to improve residue-residue contact predictions by improving the underlying mathematical models in a Bayesian framework. By explicitly modelling noise effects inherent in the underlying data and including priors to reflect the nature of residue-residue interactions, an attempt is made to reduce random and systematic errors inherent in contact prediction to make protein de novo structure prediction widely applicable

BK virus large T and VP-1 expression in infected human renal allografts

Objective. We investigated the expression of early and late phase BK virus (BKV) proteins and their interactions with host cell proteins in renal allografts, with ongoing polyomavirus associated nephropathy (PVAN), and correlated this with the nuclear and cell morphology. Methods. Frozen sections from three patients with renal allografts (two biopsies, one explant) with PVAN were analysed by indirect immunofluorescence using BKV specific anti-polyoma large T-antigen and anti-VP-1 antibodies, as well as anti-p53, anti-Ki67, anti-caspase-3, anti-bcl2 and anti-cytokeratin 22 antibodies. Nuclear morphology and size were estimated by DNA Hoechst staining. Results. In infected tubular cells the early and late phases of infection could be distinguished according to expression of large T-antigen or VP-1. The early phase revealed almost normal nuclear proportions, whereas in later phases nuclear size increased about 2 to 3 fold. Expression of large T-antigen was strongly associated with accumulation of p53 in the nucleus, accompanied by the activation of the cell cycle associated cell protein Ki67. In contrast, expression of BKV VP1 correlated only weakly with p53. Virus dependent cell lysis was due to necrosis, since neither caspase 3 nor nuclear nor cytoskeleton changes indicated apoptosis. Conclusion. In our selected patients with PVAN a clear distinction between early and late phases was possible, according to the protein expression patterns of BKV markers. Striking nuclear enlargement is only present in the late phase of infection. In the inflammatory setting of PVAN, BKV dependent effects appear to be mediated by the inhibition of p53, resulting in the activation of the cell cycle. We assume that in PVAN similar BKV mechanisms are operative as in certain in vitro system

BK virus large T and VP-1 expression in infected human renal allografts

Objective. We investigated the expression of early and late phase BK virus (BKV) proteins and their interactions with host cell proteins in renal allografts, with ongoing polyomavirus associated nephropathy (PVAN), and correlated this with the nuclear and cell morphology

Homology-based inference sets the bar high for protein function prediction

Background: Any method that de novo predicts protein function should do better than random. More challenging, it also ought to outperform simple homology-based inference. Methods: Here, we describe a few methods that predict protein function exclusively through homology. Together, they set the bar or lower limit for future improvements. Results and conclusions: During the development of these methods, we faced two surprises. Firstly, our most successful implementation for the baseline ranked very high at CAFA1. In fact, our best combination of homology-based methods fared only slightly worse than the top-of-the-line prediction method from the Jones group. Secondly, although the concept of homology-based inference is simple, this work revealed that the precise details of the implementation are crucial: not only did the methods span from top to bottom performers at CAFA, but also the reasons for these differences were unexpected. In this work, we also propose a new rigorous measure to compare predicted and experimental annotations. It puts more emphasis on the details of protein function than the other measures employed by CAFA and may best reflect the expectations of users. Clearly, the definition of proper goals remains one major objective for CAFA

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction.

Compensatory mutations between protein residues in physical contact can manifest themselves as statistical couplings between the corresponding columns in a multiple sequence alignment (MSA) of the protein family. Conversely, large coupling coefficients predict residue contacts. Methods for de-novo protein structure prediction based on this approach are becoming increasingly reliable. Their main limitation is the strong systematic and statistical noise in the estimation of coupling coefficients, which has so far limited their application to very large protein families. While most research has focused on improving predictions by adding external information, little progress has been made to improve the statistical procedure at the core, because our lack of understanding of the sources of noise poses a major obstacle. First, we show theoretically that the expectation value of the coupling score assuming no coupling is proportional to the product of the square roots of the column entropies, and we propose a simple entropy bias correction (EntC) that subtracts out this expectation value. Second, we show that the average product correction (APC) includes the correction of the entropy bias, partly explaining its success. Third, we have developed CCMgen, the first method for simulating protein evolution and generating realistic synthetic MSAs with pairwise statistical residue couplings. Fourth, to learn exact statistical models that reliably reproduce observed alignment statistics, we developed CCMpredPy, an implementation of the persistent contrastive divergence (PCD) method for exact inference. Fifth, we demonstrate how CCMgen and CCMpredPy can facilitate the development of contact prediction methods by analysing the systematic noise contributions from phylogeny and entropy. Using the entropy bias correction, we can disentangle both sources of noise and find that entropy contributes roughly twice as much noise as phylogeny

A large-scale evaluation of computational protein function prediction.

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools