BackgroundAs molecular biology is creating an increasing amount of sequence and structure data, the multitude of software to analyze this data is also rising. Most of the programs are made for a specific task, hence the user often needs to combine multiple programs in order to reach a goal. This can make the data processing unhandy, inflexible and even inefficient due to an overhead of read/write operations. Therefore, it is crucial to have a comprehensive, accessible and efficient computational biology framework in a scripting language to overcome these limitations.ResultsWe have developed the Python package Biotite: a general computational biology framework, that represents sequence and structure data based on NumPyndarrays. Furthermore the package contains seamless interfaces to biological databases and external software. The source code is freely accessible at https://github.com/biotite-dev/biotite.ConclusionsBiotite is unifying in two ways: At first it bundles popular tasks in sequence analysis and structural bioinformatics in a consistently structured package. Secondly it adresses two groups of users: novice programmers get an easy access to Biotite due to its simplicity and the comprehensive documentation. On the other hand, advanced users can profit from its high performance and extensibility. They can implement their algorithms upon Biotite, so they can skip writing code for general functionality (like file parsers) and can focus on what their software makes unique.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2367-z) contains supplementary material, which is available to authorized users.

Substantial evidence has shown that overexpression of the inhibitor of apoptosis protein (IAP) survivin in human tumors correlates significantly with treatment resistance and poor patient prognosis. Survivin serves as a radiation resistance factor that impacts the DNA damage response by interacting with DNA-dependent protein kinase (DNA-PKcs). However, the complexity, molecular determinants, and functional consequences of this interrelationship remain largely unknown. By applying coimmunoprecipitation and flow cytometry-based Förster resonance energy transfer assays, we demonstrated a direct involvement of the survivin baculovirus IAP repeat domain in the regulation of radiation survival and DNA repair. This survivin-mediated activity required an interaction of residues S20 and W67 with the phosphoinositide 3-kinase (PI3K) domain of DNA-PKcs. In silico molecular docking and dynamics simulation analyses, in vitro kinase assays, and large-scale mass spectrometry suggested a heterotetrameric survivin–DNA-PKcs complex that results in a conformational change within the DNA-PKcs PI3K domain. Overexpression of survivin resulted in enhanced PI3K enzymatic activity and detection of differentially abundant phosphopeptides and proteins implicated in the DNA damage response. The survivin–DNA-PKcs interaction altered the S/T-hydrophobic motif substrate specificity of DNA-PKcs with a predominant usage of S/T-P phosphorylation sites and an increase of DNA-PKcs substrates including Foxo3. These data demonstrate that survivin differentially regulates DNA-PKcs-dependent radiation survival and DNA double-strand break repair via formation of a survivin–DNA-PKcs heterotetrameric complex. Significance: These findings provide insight into survivin-mediated regulation of DNA-PKcs kinase and broaden our knowledge of the impact of survivin in modulating the cellular radiation response. See related commentary by Iliakis, p. 2270

Background: Visualization of multiple sequence alignments often includes colored symbols, usually characters encoding amino acids, according to some (physical) properties, such as hydrophobicity or charge. Typically, color schemes are created manually, so that equal or similar colors are assigned to amino acids that share similar properties. However, this assessment is subjective and may not represent the similarity of symbols very well. Results: In this article we propose a different approach for color scheme creation: We leverage the similarity information of a substitution matrix to derive an appropriate color scheme. Similar colors are assigned to high scoring pairs of symbols, distant colors are assigned to low scoring pairs. In order to find these optimal points in color space a simulated annealing algorithm is employed. Conclusions: Using the substitution matrix as basis for a color scheme is consistent with the alignment, which itself is based on the very substitution matrix. This approach allows fully automatic generation of new color schemes, even for special purposes which have not been covered, yet, including schemes for structural alphabets or schemes that are adapted for people with color vision deficiency.

Background Biotite is a program library for sequence and structural bioinformatics written for the Python programming language. It implements widely used computational methods into a consistent and accessible package. This allows for easy combination of various data analysis, modeling and simulation methods. Results This article presents major functionalities introduced into Biotite since its original publication. The fields of application are shown using concrete examples. We show that the computational performance of Biotite for bioinformatics tasks is comparable to individual, special purpose software systems specifically developed for the respective single task. Conclusions The results show that Biotite can be used as program library to either answer specific bioinformatics questions and simultaneously allow the user to write entire, self-contained software applications with sufficient performance for general application.

Background Most experimentally determined structures of biomolecules lack annotated hydrogen positions due to their low electron density. However, thorough structure analysis and simulations require knowledge about the positions of hydrogen atoms. Existing methods for their prediction are either limited to a certain range of molecules or only work effectively on small compounds. Results We present a novel algorithm that compiles fragments of molecules with known hydrogen atom positions into a library. Using this library the method is able to predict hydrogen positions for molecules with similar moieties. We show that the method is able to accurately assign hydrogen atoms to most organic compounds including biomacromolecules, if a sufficiently large library is used. Conclusions We bundled the algorithm into the open-source Python package and command line program . Since usually no additional parametrization is necessary for the problem at hand, the software works out-of-box for a wide range of molecular systems usually within a few seconds of computation time. Hence, we believe that could be a valuable tool for structural biologists and biophysicists alike.