MOTIVATION: Engineering high-affinity binders targeting specific antigenic determinants remains a challenging and often daunting task, requiring extensive experimental screening. Computational methods have the potential to accelerate this process, reducing costs and time, but only if they demonstrate broad applicability and efficiency in exploring mutations, evaluating affinity, and pruning unproductive mutation paths. RESULTS: In response to these challenges, we introduce a new computational platform for optimizing protein binders towards their targets. The platform is organized as a series of modules, performing mutation selection and application, molecular dynamics simulations to sample conformations around interaction poses, and mutation prioritization using suitable scoring functions. Notably, the platform supports parallel exploration of different mutation streams, enabling in silico high-throughput screening on High Performance Computing (HPC) systems. Furthermore, the platform is highly customizable, allowing users to implement their own protocols. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/pgbarletta/locuaz and documentation is at https://locuaz.readthedocs.io/. The data underlying this article are available at https://github.com/pgbarletta/suppl_info_locuaz.

SUMMARY: NanoShaper is a program specifically aiming the construction and analysis of the molecular surface of nanoscopic systems. It uses ray-casting for parallelism and it performs analytical computations whenever possible to maximize robustness and accuracy of the approach. Among the other features, NanoShaper provides volume, surface area, including that of internal cavities, for any considered molecular system. It identifies pockets via a very intuitive definition based on the concept of probe radius, intrinsic to the definition of the solvent excluded surface. We show here that, with a suitable choice of the parameters, the same approach can also permit the visualisation of molecular channels. NanoShaper has now been interfaced with the widely used molecular visualization software VMD, further enriching its already well furnished toolset. AVAILABILITY AND IMPLEMENTATION: VMD is available at http://www.ks.uiuc.edu/Research/vmd/. NanoShaper, its documentation, tutorials and supporting programs are available at http://concept.iit.it/downloads. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, we introduce the concept of principal paths in data space; we show that this is a well-characterized problem from the point of view of cognition, and that it can lead to salient insights in the analyzed data enabling topological/holistic descriptions. These paths, interestingly, can be interpreted as local principal curves, and in this paper, we suggest that they are analogous to what, in the statistical mechanics realm, are called minimum free-energy paths. Here, we move that concept from physics to data space and compute them in both the original and the kernel space. The algorithm is a regularized version of the well-known k -means clustering algorithm. The regularization parameter is derived via an in-sample model selection process based on the Bayesian evidence maximization. Interestingly, we show that this choice for the regularization parameter consistently leads to the same manifold even when changing the number of clusters. We apply the method to common data sets, dynamical systems, and, in particular, to molecular dynamics trajectories showing the generality, the usefulness of the approach and its superiority with respect to other related approaches.

Recognizing the samples belonging to one class in a heterogeneous data set is a very interesting but tough machine learning task. Some samples of the data set can be actual outliers or members of other classes for which training examples are lacking. In contrast to other kernel approaches present in the literature, in this work, the problem is faced defining a one-class kernel machine that delivers the probability for a sample to belong to the support of the distribution and that can be efficiently trained by a hybrid sequential minimal optimization-expectation maximization algorithm. Due to the analogy to the import vector machine and to the one-class approach, we named the method import vector domain description (IVDD). IVDD was tested on a toy 2-D data set in order to characterize its behavior on a set of widely used benchmarking UCI data sets and, lastly, challenged against a real world outlier detection data set. All the results were compared against state-of-the-art closely related methods such as one-class-SVM and Support Vector Domain Description, proving that the algorithm is equally accurate with the additional advantage of delivering the probability estimate for each sample. Finally, a few variants aimed at providing memory savings and/or computational speed-up in the light of big data analysis are briefly sketched.

SUMMARY: In this work, we present a CUDA-based GPU implementation of a Poisson-Boltzmann equation solver, in both the linear and non-linear versions, using double precision. A finite difference scheme is adopted and made suitable for the GPU architecture. The resulting code was interfaced with the electrostatics software for biomolecules DelPhi, which is widely used in the computational biology community. The algorithm has been implemented using CUDA and tested over a few representative cases of biological interest. Details of the implementation and performance test results are illustrated. A speedup of ~10 times was achieved both in the linear and non-linear cases. AVAILABILITY AND IMPLEMENTATION: The module is open-source and available at http://www.electrostaticszone.eu/index.php/downloads.

SUMMARY A proper and efficient representation of possibly complex and large molecular surfaces is an important task in bioinformatics and biophysics. Molecular surfaces indeed are used for different aims, in particular, for computation, as visual support tools for biologists, in electrostatics problems involving implicit solvents (e.g. while solving the Poisson–Boltzmann equation) or for molecular dynamics simulations. This is the reason why, in literature, a multitude of algorithms that differ on the basis of the adopted representation and the approach/technology used were proposed. In this paper we present a CUDA‐based software component able to produce high‐resolution molecular surfaces based on the Van der Waals, solvent accessible, Richards–Connolly and blobby definitions. The component was designed to be used in heterogeneous visualization pipelines; therefore, the representation of the molecular surfaces suits both the direct visualization and the efficient storing for following processing steps. Experimental results show speedup figures between 39.3 and 88.4 considering molecules of different sizes and surface resolutions, resulting in meshes of up to 258.6 million triangles. Copyright © 2013 John Wiley & Sons, Ltd.

In this work an implementation of a linearized Poisson-Boltzmann equation solver based on a Finite Differences scheme on the GPU architecture is presented. The algorithm exploits the checkerboard structure of the discretized Laplace operator and follows the footprints of a popular solver called DelPhi, which is widely used in the Computational Biology community. The algorithm has been implemented using CUDA. This implementation has then been integrated with the DelPhi solver and tested over a few representative cases of biological interest. Details of the implementation as well as performance test results are illustrated.