Improvements to the APBS biomolecular solvation software suite

The Adaptive Poisson-Boltzmann Solver (APBS) software was developed to solve the equations of continuum electrostatics for large biomolecular assemblages that has provided impact in the study of a broad range of chemical, biological, and biomedical applications. APBS addresses three key technology challenges for understanding solvation and electrostatics in biomedical applications: accurate and efficient models for biomolecular solvation and electrostatics, robust and scalable software for applying those theories to biomolecular systems, and mechanisms for sharing and analyzing biomolecular electrostatics data in the scientific community. To address new research applications and advancing computational capabilities, we have continually updated APBS and its suite of accompanying software since its release in 2001. In this manuscript, we discuss the models and capabilities that have recently been implemented within the APBS software package including: a Poisson-Boltzmann analytical and a semi-analytical solver, an optimized boundary element solver, a geometry-based geometric flow solvation model, a graph theory based algorithm for determining p$K_a$ values, and an improved web-based visualization tool for viewing electrostatics

A new approach to immunoFET operation

A new method is presented for the detection of an immunological reaction taking place in a membrane, which covers the gate area of an ISFET. By stepwise changing the electrolyte concentration of the sample solution, a transient diffusion of ions through the membrane-protein layer occurs, resulting in a transient membrane potential, which is measured by the ISFET. The diffusion rate is determined by the immobile charge density in the amphoteric protein layer, which changes upon formation of antibody—antigen complexes. No membrane potential is induced at zero fixed charge density as occurs at a protein characteristic pH. Isoeletric points of embedded proteins can be determined by detecting the zero potential response.\ud \ud Up to now, the authors have studied the membrane adsorption of lysozyme, human serum albumin (HSA) and the immune reaction of HSA with the antibody anti-human serum albumin (αHSA). The influence of protein parameters on the amplitude of the transient can be described with an empirical equation. Assuming Langmuir behaviour, the protein concentration in the solution can well be correlated with the concentration in the membrane.\ud \ud This new detection method is unique concerning direct measurements of charge densities and isoelectric points of amphoteric macromolecules adsorbed in the membrane. The simple procedure of one incubation stage followed by one detection stage, without separate washing and labelling techniques, gives direct information about specific charge properties of the macromolecules to be studied

TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the entangled geometric complexity and biological complexity. We introduce topology, i.e., element specific persistent homology (ESPH), to untangle geometric complexity and biological complexity. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains crucial biological information via a multichannel image representation. It is able to reveal hidden structure-function relationships in biomolecules. We further integrate ESPH and convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the limitations to deep learning arising from small and noisy training sets, we present a multitask topological convolutional neural network (MT-TCNN). We demonstrate that the present TopologyNet architectures outperform other state-of-the-art methods in the predictions of protein-ligand binding affinities, globular protein mutation impacts, and membrane protein mutation impacts.Comment: 20 pages, 8 figures, 5 table