MolOptimizer: A Molecular Optimization Toolkit for Fragment-Based Drug Design.

MolOptimizer is a user-friendly computational toolkit designed to streamline the hit-to-lead optimization process in drug discovery. MolOptimizer extracts features and trains machine learning models using a user-provided, labeled, and small-molecule dataset to accurately predict the binding values of new small molecules that share similar scaffolds with the target in focus. Hosted on the Azure web-based server, MolOptimizer emerges as a vital resource, accelerating the discovery and development of novel drug candidates with improved binding properties.

Fast, accurate, and cost-effective poultry sex genotyping using real-time polymerase chain reaction.

According to The Organization for Economic Co-operation and Development (OECD), demand for poultry meat and eggs consumption is growing consistently since poultry meat and eggs are readily available and cheap source for nutritional protein. As such, there is pressing demand from industry for improved protocols to determine chicken sex, especially in layer industry since only females can lay eggs. Extensive efforts are being dedicated to avoiding male chicks culling by developing in-ovo sexing detection methods. Any established in-ovo detection method will need to be validated by embryo genotyping. Therefore, there is a growing demand for fast, inexpensive, and precise method for proper discrimination between males and females in the poultry science community. Our aim with this study was to develop an accurate, high-throughput protocol for sex determination using small volumes of blood. We designed primers targeting the Hint-W gene within the W chromosome clearly distinguishing between males and females. In the interest of establishing an efficient protocol without the need for gel electrophoresis, crude DNA extraction without further purification was coupled with qPCR. We validated the accuracy of our method using established protocols and gonad phenotyping and tested our protocol with four different chicken breeds, day-nine embryos, day-old chicks and adult chicken. In summary, we developed a fast, cost-effective, and accurate method for the genotyping of sex chromosomes in chicken.

Predicting Churn in Online Games by Quantifying Diversity of Engagement.

Understanding engagement patterns of users in online platforms, may it be games, online social networks, or academic websites, is a widely studied topic with many real-world applications and economic consequences. A holy grail in this area of research is to develop an automatic prediction algorithm for when a user is going to leave the platform and devise proper intervention. In this work, we study online recreational games and propose to model the engagement patterns of players through an unsupervised learning framework. We think of engagement as a continuous temporal process, measured along specific axes derived from gaming users' data using principal component analysis. We track the overall trend of the projection of the data along the significant principal components. We find that the geometric variability of the trajectory is a good predictor of the users' engagement level. Users characterized by a time series with large variability are users with higher engagement; namely, they will continue playing the game for prolonged periods of time. We evaluated our methodology on two data sets of very different game types and compared the performance of our method with state-of-the-art black-box machine learning algorithms. Our results were fairly competitive with these methods, and we conclude that churn can be predicted using an explainable, intuitive, and white-box decision-rule algorithm.

The Compositionality of Facial Expressions.

The principle of compositionality, an important postulation in language and cognition research, posits that the meaning of a complex expression is determined by the meaning of its constituting parts and the operation performed on those parts. Here, we provide strong evidence that this principle plays a significant role also in interpreting facial expressions. In three studies in which perceivers interpreted sequences of two emotional facial expression images, we show that the composite meaning of facial expressions results from the meaning of its constituting expressions and an algebraic operation performed on those expressions. Our study offers a systematic account as to how the meaning of facial expressions (single and sequences) are being formed and perceived. In a broader context, our results raise the possibility that the principle of compositionality may apply to human communication modalities beyond spoken language, whereby a minimal number of components are expanded to a much greater number of meanings.

Inferring primase-DNA specific recognition using a data driven approach.

DNA-protein interactions play essential roles in all living cells. Understanding of how features embedded in the DNA sequence affect specific interactions with proteins is both challenging and important, since it may contribute to finding the means to regulate metabolic pathways involving DNA-protein interactions. Using a massive experimental benchmark dataset of binding scores for DNA sequences and a machine learning workflow, we describe the binding to DNA of T7 primase, as a model system for specific DNA-protein interactions. Effective binding of T7 primase to its specific DNA recognition sequences triggers the formation of RNA primers that serve as Okazaki fragment start sites during DNA replication.

From physical to social interactions: The relative entropy model.

Complex social systems at various scales of analysis (e.g. dyads, families, tribes, etc.) are formed and maintained through verbal interactions. Therefore, the ability to (1) model these interactions and (2) to use models of interaction for identifying significant relations may be of interest to the social sciences. Adopting the perspective of social physics, we present a general approach for modeling interactions through relative entropy. For illustrating the benefits of the approach, we derive measures of "perspective-taking" and use them for identifying significant-romantic relations in a data set composed of the verbal interactions taken place at the famous TV series "Sex and the City". Using these measures, we show that significant-romantic relations can be identified with success. These results provide preliminary support for the benefits of using the proposed approach.

Discovery of small-molecule inhibitors targeting the ribosomal peptidyl transferase center (PTC) of M. tuberculosis.

M. tuberculosis (Mtb) is a pathogenic bacterium that causes tuberculosis, which kills more than 1.5 million people worldwide every year. Strains resistant to available antibiotics pose a significant healthcare problem. The enormous complexity of the ribosome poses a barrier for drug discovery. We have overcome this in a tractable way by using an RNA segment that represents the peptidyl transferase center as a target. By using a novel combination of NMR transverse relaxation times (T 2) and computational chemistry approaches, we have obtained improved inhibitors of the Mtb ribosomal PTC. Two phenylthiazole derivatives were predicted by machine learning models as effective inhibitors, and this was confirmed by their IC50 values, which were significantly improved over standard antibiotic drugs.

The Adaptive Behavior of a Soccer Team: An Entropy-Based Analysis.

To optimize its performance, a competitive team, such as a soccer team, must maintain a delicate balance between organization and disorganization. On the one hand, the team should maintain organized patterns of behavior to maximize the cooperation between its members. On the other hand, the team's behavior should be disordered enough to mislead its opponent and to maintain enough degrees of freedom. In this paper, we have analyzed this dynamic in the context of soccer games and examined whether it is correlated with the team's performance. We measured the organization associated with the behavior of a soccer team through the Tsallis entropy of ball passes between the players. Analyzing data taken from the English Premier League (2015/2016), we show that the team's position at the end of the season is correlated with the team's entropy as measured with a super-additive entropy index. Moreover, the entropy score of a team significantly contributes to the prediction of the team's position at the end of the season beyond the prediction gained by the team's position at the end of the previous season.

Modulation of RNA primer formation by Mn(II)-substituted T7 DNA primase.

Lagging strand DNA synthesis by DNA polymerase requires RNA primers produced by DNA primase. The N-terminal primase domain of the gene 4 protein of phage T7 comprises a zinc-binding domain that recognizes a specific DNA sequence and an RNA polymerase domain that catalyzes RNA polymerization. Based on its crystal structure, the RNA polymerase domain contains two Mg(II) ions. Mn(II) substitution leads to elevated RNA primer synthesis by T7 DNA primase. NMR analysis revealed that upon binding Mn(II), T7 DNA primase undergoes conformational changes near the metal cofactor binding site that are not observed when the enzyme binds Mg(II). A machine-learning algorithm called linear discriminant analysis (LDA) was trained by using the large collection of Mn(II) and Mg(II) binding sites available in the protein data bank (PDB). Application of the model to DNA primase revealed a preference in the enzyme's second metal binding site for Mn(II) over Mg(II), suggesting that T7 DNA primase activity modulation when bound to Mn(II) is based on structural changes in the enzyme.