The increasing number of scientific publications focusing on magnetic materials indicates growing interest in the broader scientific community. Substantial progress was made in the synthesis of magnetic materials of desired size, morphology, chemical composition, and surface chemistry. Physical and chemical stability of magnetic materials is acquired by the coating. Moreover, surface layers of polymers, silica, biomolecules, etc. can be designed to obtain affinity to target molecules. The combination of the ability to respond to the external magnetic field and the rich possibilities of coatings makes magnetic materials universal tool for magnetic separations of small molecules, biomolecules and cells. In the biomedical field, magnetic particles and magnetic composites are utilized as the drug carriers, as contrast agents for magnetic resonance imaging (MRI), and in magnetic hyperthermia. However, the multifunctional magnetic particles enabling the diagnosis and therapy at the same time are emerging. The presented review article summarizes the findings regarding the design and synthesis of magnetic materials focused on biomedical applications. We highlight the utilization of magnetic materials in separation/preconcentration of various molecules and cells, and their use in diagnosis and therapy.

Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.

Prostate cancer (CaP) is the most common type of tumour disease in men. Early diagnosis of cancer of the prostate is very important, because the sooner the cancer is detected, the better it is treated. According to that fact, there is great interest in the finding of new markers including amino acids, proteins or nucleic acids. Prostate specific antigen (PSA) is commonly used and is the most important biomarker of CaP. This marker can only be detected in blood and its sensitivity is approximately 80%. Moreover, early stages cannot be diagnosed using this protein. Currently, there does not exist a test for diagnosis of early stages of prostate cancer. This fact motivates us to find markers sensitive to the early stages of CaP, which are easily detected in body fluids including urine. A potential is therefore attributed to the non-protein amino acid sarcosine, which is generated by glycine-N-methyltransferase in its biochemical cycle. In this review, we summarize analytical methods for quantification of sarcosine as a CaP marker. Moreover, pathways of the connection of synthesis of sarcosine and CaP development are discussed.

The purpose of this quick guide is to help new modelers who have little or no background in comparative modeling yet are keen to produce high-resolution protein 3D structures for their study by following systematic good modeling practices, using affordable personal computers or online computational resources. Through the available experimental 3D-structure repositories, the modeler should be able to access and use the atomic coordinates for building homology models. We also aim to provide the modeler with a rationale behind making a simple list of atomic coordinates suitable for computational analysis abiding to principles of physics (e.g., molecular mechanics). Keeping that objective in mind, these quick tips cover the process of homology modeling and some postmodeling computations such as molecular docking and molecular dynamics (MD). A brief section was left for modeling nonprotein molecules, and a short case study of homology modeling is discussed.

The threat of a worldwide influenza pandemic has greatly increased over the past decade with the emergence of highly virulent avian influenza strains. The increased frequency of drug-resistant influenza strains against currently available antiviral drugs requires urgent development of new strategies for antiviral therapy, too. The research in the field of therapeutic peptides began to develop extensively in the second half of the 20^th century. Since then, the mechanisms of action for several peptides and their antiviral prospect received large attention due to the global threat posed by viruses. Here, we discussed the therapeutic properties of peptides used in influenza treatment. Peptides with antiviral activity against influenza can be divided into three main groups. First, entry blocker peptides such as a Flupep that interact with influenza hemagglutinin, block its binding to host cells and prevent viral fusion. Second, several peptides display virucidal activity, disrupting viral envelopes, e.g., Melittin. Finally, a third set of peptides interacts with the viral polymerase complex and act as viral replication inhibitors such as PB1 derived peptides. Here, we present a review of the current literature describing the antiviral activity, mechanism and future therapeutic potential of these influenza antiviral peptides.