Artificial intelligence-driven antimicrobial peptide discovery

Antimicrobial peptides (AMPs) emerge as promising agents against antimicrobial resistance, providing an alternative to conventional antibiotics. Artificial intelligence (AI) revolutionized AMP discovery through both discrimination and generation approaches. The discriminators aid the identification of promising candidates by predicting key peptide properties such as activity and toxicity, while the generators learn the distribution over peptides and enable sampling novel AMP candidates, either de novo, or as analogues of a prototype peptide. Moreover, the controlled generation of AMPs with desired properties is achieved by discriminator-guided filtering, positive-only learning, latent space sampling, as well as conditional and optimized generation. Here we review recent achievements in AI-driven AMP discovery, highlighting the most exciting directions

Inferring the paths of somatic evolution in cancer

Motivation: Cancer cell genomes acquire several genetic alterations during somatic evolution from a normal cell type. The relative order in which these mutations accumulate and contribute to cell fitness is affected by epistatic interactions. Inferring their evolutionary history is challenging because of the large number of mutations acquired by cancer cells as well as the presence of unknown epistatic interactions. Results: We developed Bayesian Mutation Landscape (BML), a probabilistic approach for reconstructing ancestral genotypes from tumor samples for much larger sets of genes than previously feasible. BML infers the likely sequence of mutation accumulation for any set of genes that is recurrently mutated in tumor samples. When applied to tumor samples from colorectal, glioblastoma, lung and ovarian cancer patients, BML identifies the diverse evolutionary scenarios involved in tumor initiation and progression in greater detail, but broadly in agreement with prior results. Availability and implementation: Source code and all datasets are freely available at bml.molgen.mpg.de Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Efficient seeding techniques for protein similarity search

We apply the concept of subset seeds proposed in [1] to similarity search in protein sequences. The main question studied is the design of efficient seed alphabets to construct seeds with optimal sensitivity/selectivity trade-offs. We propose several different design methods and use them to construct several alphabets.We then perform an analysis of seeds built over those alphabet and compare them with the standard Blastp seeding method [2,3], as well as with the family of vector seeds proposed in [4]. While the formalism of subset seed is less expressive (but less costly to implement) than the accumulative principle used in Blastp and vector seeds, our seeds show a similar or even better performance than Blastp on Bernoulli models of proteins compatible with the common BLOSUM62 matrix

Efficient seeding techniques for protein similarity search

We apply the concept of subset seeds proposed in [1] to similarity search in protein sequences. The main question studied is the design of efficient seed alphabets to construct seeds with optimal sensitivity/selectivity trade-offs. We propose several different design methods and use them to construct several alphabets.We then perform an analysis of seeds built over those alphabet and compare them with the standard Blastp seeding method [2,3], as well as with the family of vector seeds proposed in [4]. While the formalism of subset seed is less expressive (but less costly to implement) than the accumulative principle used in Blastp and vector seeds, our seeds show a similar or even better performance than Blastp on Bernoulli models of proteins compatible with the common BLOSUM62 matrix

The R Package bgmm : Mixture Modeling with Uncertain Knowledge

Classical supervised learning enjoys the luxury of accessing the true known labels for the observations in a modeled dataset. Real life, however, poses an abundance of problems, where the labels are only partially defined, i.e., are uncertain and given only for a subsetof observations. Such partial labels can occur regardless of the knowledge source. For example, an experimental assessment of labels may have limited capacity and is prone to measurement errors. Also expert knowledge is often restricted to a specialized area and is thus unlikely to provide trustworthy labels for all observations in the dataset. Partially supervised mixture modeling is able to process such sparse and imprecise input. Here, we present an R package calledbgmm, which implements two partially supervised mixture modeling methods: soft-label and belief-based modeling. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. On real data we present the usage of bgmm for basic model-fitting in all modeling variants. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. This functionality is presented on an artificial dataset, which can be simulated in bgmm from a distribution defined by a given model

Comprehensive prediction in 78 human cell lines reveals rigidity and compactness of transcription factor dimers

The binding of transcription factors (TFs) to their specific motifs in genomic regulatory regions is commonly studied in isolation. However, in order to elucidate the mechanisms of transcriptional regulation, it is essential to determine which TFs bind DNA cooperatively as dimers and to infer the precise nature of these interactions. So far, only a small number of such dimeric complexes are known. Here, we present an algorithm for predicting cell-type-specific TF-TF dimerization on DNA on a large scale, using DNase I hypersensitivity data from 78 human cell lines. We represented the universe of possible TF complexes by their corresponding motif complexes, and analyzed their occurrence at cell-type-specific DNase I hypersensitive sites. Based on ~1.4 billion tests for motif complex enrichment, we predicted 603 highly significant celltype- specific TF dimers, the vast majority of which are novel. Our predictions included 76% (19/25) of the known dimeric complexes and showed significant overlap with an e xperimental database of protein-protein interactions. They were also independently supported by evolutionary conservation, as well as quantitative variation in DNase I digestion patterns. Notably, the known and predicted TF dimers were almost always highly compact and rigidly spaced, suggesting that TFs dimerize in close proximity to their partners, which results in strict constraints on the structure of the DNA-bound complex. Overall, our results indicate that chromatin openness profiles are highly predictive of cell-type-specific TF-TF interactions. Moreover, cooperative TF dimerization seems to be a widespread phenomenon, with multiple TF complexes predicted in most cell types. © 2013, Published by Cold Spring Harbor Laboratory Press.Link_to_subscribed_fulltex

Wprowadzenie

"Doświadczenia społeczne i praktyka życia codziennego potwierdzają potrzebę ustawicznych badań i analiz zjawiska wielokulturowości, kształtowania się tożsamości, edukacji grup mniejszościowych i etnicznych oraz relacji między reprezentantami różnych narodów, religii, kultur. Nie brak współcześnie wydarzeń ilustrujących skalę i złożoność problemów związanych z wielokulturowością, wywołanych między innymi narastaniem postaw ksenofobicznych, niechęci, uprzedzeń, ale i sprzecznością interesów politycznych, społecznych i gospodarczych. Są też zjawiska świadczące o dobrym rozumieniu wielokulturowości i radzenia sobie z nią w życiu codziennym w rodzinie, szkole, środowisku lokalnym." [...] (fragm.

Z teorii i praktyki edukacji międzykulturowej

The article presents a twenty year activity and research done by academics of the Department of General Pedagogy on the issue of the borderland as well as multi- and intercultural education. The research of the very team concern social, pedagogical and cultural conditions of the development and education of the youth from the borderland, the problems of shaping identity, theory and intercultural models, as well as teacher preparation for working in multicultural communities

Problematyka wielokulturowości oraz edukacji międzykulturowej w działalności naukowo-badawczej Katedry Pedagogiki Ogólnej

The Department of General Pedagogy at the Institute of the Sciences of Education at the Faculty of Ethnology and Sciences of Education in Cieszyn has conducted the interdisciplinary studies on Social pedagogical and cultural conditions o f development and education o f children and youth in the borderline under the supervision of professor Tadeusz Lewowicki since 1990 (as the Department of General Pedagogy at that time). The participants of these studies, apart from the academic staff, constitute the members of other academic centres (from Białystok, Gdańsk, Opole, Poznań, Warszawa, Zielona Góra), affiliated with Społeczny Zespół Badań Kultury i Oświaty Pogranicza (Social Team of Boundary Culture and Education Studies) between 1994 and 2004 and, currently, with Zespół Pedagogiki Kultury i Edukacji Międzykulturowej KNP PAN (the Team of Pedagogy of Culture and Intercultural Education). The academic-research works conducted at the Department of General pedagogy within the years oscillated around the issue of intercultural education in the borderline, as well as training teachers and pedagogues for work in multicultural communities