The CRISPR-Cas9 system has been employed to generate mutant alleles in a range of different organisms. However, so far there have not been reports of use of this system for efficient correction of a genetic disease. Here we show that mice with a dominant mutation in Crygc gene that causes cataracts could be rescued by coinjection into zygotes of Cas9 mRNA and a single-guide RNA (sgRNA) targeting the mutant allele. Correction occurred via homology-directed repair (HDR) based on an exogenously supplied oligonucleotide or the endogenous WT allele, with only rare evidence of off-target modifications. The resulting mice were fertile and able to transmit the corrected allele to their progeny. Thus, our study provides proof of principle for use of the CRISPR-Cas9 system to correct genetic disease.

The original, online version of this article contained a graphical abstract that was mislabeled, with the female somatic cells being labeled with AMH and the male cells being labeled with BMP2. The corrected graphical abstract, with the female somatic cells labeled with BMP2 and the male somatic cells labeled with AMH, is now included with both the online and print versions of our article. Both the erroneous and corrected graphical abstracts are also included below for the sake of comparison. We apologize for our initial mistake.

Nucleic acids have been widely recognized as potential targets in drug discovery and aptamer selection. Quantifying the interactions between small molecules and nucleic acids is critical to discover lead compounds and design novel aptamers. Scoring function is normally employed to quantify the interactions in structure-based virtual screening. However, the predictive power of nucleic acid–ligand scoring functions is still a challenge compared to other types of biomolecular recognition. With the rapid growth of experimentally determined nucleic acid–ligand complex structures, in this work, we develop a knowledge-based scoring function of nucleic acid–ligand interactions, namely SPA-LN. SPA-LN is optimized by maximizing both the affinity and specificity of native complex structures. The development strategy is different from those of previous nucleic acid–ligand scoring functions which focus on the affinity only in the optimization. The native conformation is stabilized while non-native conformations are destabilized by our optimization, making the funnel-like binding energy landscape more biased toward the native state. The performance of SPA-LN validates the development strategy and provides a relatively more accurate way to score the nucleic acid–ligand interactions.

Highly efficient and specific biomolecular recognition requires both affinity and specificity. Previous quantitative descriptions of biomolecular recognition were mostly driven by improving the affinity prediction, but lack of quantification of specificity. We developed a novel method SPA (SPecificity and Affinity) based on our funneled energy landscape theory. The strategy is to simultaneously optimize the quantified specificity of the “native” protein-ligand complex discriminating against “non-native” binding modes and the affinity prediction. The benchmark testing of SPA shows the best performance against 16 other popular scoring functions in industry and academia on both prediction of binding affinity and “native” binding pose. For the target COX-2 of nonsteroidal anti-inflammatory drugs, SPA successfully discriminates the drugs from the diversity set, and the selective drugs from non-selective drugs. The remarkable performance demonstrates that SPA has significant potential applications in identifying lead compounds for drug discovery.

Binding affinity and specificity are crucial for biomolecular recognition. Past studies have focused on binding affinity while the quantification of specificity has remained an elusive challenge. The conventional specificity measures the discrimination of the specific receptor against others for a ligand binding. It is difficult to explore all the possible competing receptors for the ligand. Here, we quantified the thermodynamic intrinsic specificity of discriminating the "native" binding mode against the "nonnative" binding modes. Intrinsic specificity is relatively easy to compute since one doesn't need to explore all the other receptors. We found that the thermodynamic intrinsic specificity correlates with the conventional specificity. This validates the statistical equivalence of conventional and intrinsic specificities for receptors at reasonable size. We also computationally quantified the residence time of a ligand on the receptor target as the kinetic specificity. We found that the kinetic specificity correlates with the thermodynamic intrinsic specificity and the binding affinity, suggesting the kinetics and the thermodynamics can be simultaneously optimized for biological activities. With the thermodynamic and kinetic specificities in addition to affinity, we carried out a drug screening test on the target cyclooxygenase-2 (COX-2). We showed that multidimensional (two-and three-dimensional) screening has higher capability than affinity alone to discriminate the drug target (COX-2) from the competitive receptor (COX-1), and the selective drugs from the non-selective drugs. Our work suggests a new way of multidimensional drug screening and target identification, which has significant potential applications for drug discovery and design.

The algorithm is implemented in C language, and the code can be downloaded from http://dl.dropbox.com/u/1865642/Optimization.cpp.

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SummaryProtein folding is an important and challenging problem in molecular biology. During the last two decades, molecular dynamics (MD) simulation has proved to be a paramount tool and was widely used to study protein structures, folding kinetics and thermodynamics, and structure-stability-function relationship. It was also used to help engineering and designing new proteins, and to answer even more general questions such as the minimal number of amino acid or the evolution principle of protein families. Nowadays, the MD simulation is still undergoing rapid developments. The first trend is to toward developing new coarse-grained models and studying larger and more complex molecular systems such as protein-protein complex and their assembling process, amyloid related aggregations, and structure and motion of chaperons, motors, channels and virus capsides; the second trend is toward building high resolution models and explore more detailed and accurate pictures of protein folding and the associated processes, such as the coordination bond or disulfide bond involved folding, the polarization, charge transfer and protonate/deprotonate process involved in metal coupled folding, and the ion permeation and its coupling with the kinetics of channels. On these new territories, MD simulations have given many promising results and will continue to offer exciting views. Here, we review several new subjects investigated by using MD simulations as well as the corresponding developments of appropriate protein models. These include but are not limited to the attempt to go beyond the topology based Gō-like model and characterize the energetic factors in protein structures and dynamics, the study of the thermodynamics and kinetics of disulfide bond involved protein folding, the modeling of the interactions between chaperonin and the encapsulated protein and the protein folding under this circumstance, the effort to clarify the important yet still elusive folding mechanism of protein BBL, the development of discrete MD and its application in studying the a-b conformational conversion and oligomer assembling process, and the modeling of metal ion involved protein folding.