BackgroundAccurate modeling of electrostatic potential and corresponding energies becomes increasingly important for understanding properties of biological macromolecules and their complexes. However, this is not an easy task due to the irregular shape of biological entities and the presence of water and mobile ions.ResultsHere we report a comprehensive suite for the well-known Poisson-Boltzmann solver, DelPhi, enriched with additional features to facilitate DelPhi usage. The suite allows for easy download of both DelPhi executable files and source code along with a makefile for local installations. The users can obtain the DelPhi manual and parameter files required for the corresponding investigation. Non-experienced researchers can download examples containing all necessary data to carry out DelPhi runs on a set of selected examples illustrating various DelPhi features and demonstrating DelPhi’s accuracy against analytical solutions.ConclusionsDelPhi suite offers not only the DelPhi executable and sources files, examples and parameter files, but also provides links to third party developed resources either utilizing DelPhi or providing plugins for DelPhi. In addition, the users and developers are offered a forum to share ideas, resolve issues, report bugs and seek help with respect to the DelPhi package. The resource is available free of charge for academic users from URL: http://compbio.clemson.edu/DelPhi.php.

Genetic variations resulting in a change of amino acid sequence can have a dramatic effect on stability, hydrogen bond network, conformational dynamics, activity and many other physiologically important properties of proteins. The substitutions of only one residue in a protein sequence, so-called missense mutations, can be related to many pathological conditions, and may influence susceptibility to disease and drug treatment. The plausible effects of missense mutations range from affecting the macromolecular stability to perturbing macromolecular interactions and cellular localization. Here we review the individual cases and genome-wide studies which illustrate the association between missense mutations and diseases. In addition we emphasize that the molecular mechanisms of effects of mutations should be revealed in order to understand the disease origin. Finally we report the current state-of-the-art methodologies which predict the effects of mutations on protein stability, the hydrogen bond network, pH-dependence, conformational dynamics and protein function.

'Salt & Pepper' syndrome is an autosomal recessive condition characterized by severe intellectual disability, epilepsy, scoliosis, choreoathetosis, dysmorphic facial features and altered dermal pigmentation. High-density SNP array analysis performed on siblings first described with this syndrome detected four shared regions of loss of heterozygosity (LOH). Whole-exome sequencing narrowed the candidate region to chromosome 2p11.2. Sanger sequencing confirmed a homozygous c.994G>A transition (p.E332K) in the ST3GAL5 gene, which encodes for a sialyltransferase also known as GM3 synthase. A different homozygous mutation of this gene has been previously associated with infantile-onset epilepsy syndromes in two other cohorts. The ST3GAL5 enzyme synthesizes ganglioside GM3, a glycosophingolipid enriched in neural tissue, by adding sialic acid to lactosylceramide. Unlike disorders of glycosphingolipid (GSL) degradation, very little is known regarding the molecular and pathophysiologic consequences of altered GSL biosynthesis. Glycolipid analysis confirmed a complete lack of GM3 ganglioside in patient fibroblasts, while microarray analysis of glycosyltransferase mRNAs detected modestly increased expression of ST3GAL5 and greater changes in transcripts encoding enzymes that lie downstream of ST3GAL5 and in other GSL biosynthetic pathways. Comprehensive glycomic analysis of N-linked, O-linked and GSL glycans revealed collateral alterations in response to loss of complex gangliosides in patient fibroblasts and in zebrafish embryos injected with antisense morpholinos that targeted zebrafish st3gal5 expression. Morphant zebrafish embryos also exhibited increased apoptotic cell death in multiple brain regions, emphasizing the importance of GSL expression in normal neural development and function.

This review emphasizes the effects of naturally occurring mutations on structural features and physico-chemical properties of proteins. The basic protein characteristics considered are stability, dynamics, and the binding of proteins and methods for assessing effects of mutations on these macromolecular characteristics are briefly outlined. It is emphasized that the above entities mostly reflect global characteristics of considered macromolecules, while given mutations may alter the local structural features such as salt bridges and hydrogen bonds without affecting the global ones. Furthermore, it is pointed out that disease-causing mutations frequently involve a drastic change of amino acid physico-chemical properties such as charge, hydrophobicity, and geometry, and are less surface exposed than polymorphic mutations.

The crucial prerequisite for proper biological function is the protein’s ability to establish highly selective interactions with macromolecular partners. A missense mutation that alters the protein binding affinity may cause significant perturbations or complete abolishment of the function, potentially leading to diseases. The availability of computational methods to evaluate the impact of mutations on protein–protein binding is critical for a wide range of biomedical applications. Here, we report an efficient computational approach for predicting the effect of single and multiple missense mutations on protein–protein binding affinity. It is based on a well-tested simulation protocol for structure minimization, modified MM-PBSA and statistical scoring energy functions with parameters optimized on experimental sets of several thousands of mutations. Our simulation protocol yields very good agreement between predicted and experimental values with Pearson correlation coefficients of 0.69 and 0.63 and root-mean-square errors of 1.20 and 1.90 kcal mol–1 for single and multiple mutations, respectively. Compared with other available methods, our approach achieves high speed and prediction accuracy and can be applied to large datasets generated by modern genomics initiatives. In addition, we report a crucial role of water model and the polar solvation energy in estimating the changes in binding affinity. Our analysis also reveals that prediction accuracy and effect of mutations on binding strongly depends on the type of mutation and its location in a protein complex.