Modeling the effect of mutations on protein thermodynamics stability is useful for protein engineering and understanding molecular mechanisms of disease-causing variants. Here, we report a new development of the SAAFEC method, the SAAFEC-SEQ, which is a gradient boosting decision tree machine learning method to predict the change of the folding free energy caused by amino acid substitutions. The method does not require the 3D structure of the corresponding protein, but only its sequence and, thus, can be applied on genome-scale investigations where structural information is very sparse. SAAFEC-SEQ uses physicochemical properties, sequence features, and evolutionary information features to make the predictions. It is shown to consistently outperform all existing state-of-the-art sequence-based methods in both the Pearson correlation coefficient and root-mean-squared-error parameters as benchmarked on several independent datasets. The SAAFEC-SEQ has been implemented into a web server and is available as stand-alone code that can be downloaded and embedded into other researchers’ code.

Electrostatic potential, energies, and forces affect virtually any process in molecular biology, however, computing these quantities is a difficult task due to irregularly shaped macromolecules and the presence of water. Here, we report a new edition of the popular software package DelPhi along with describing its functionalities. The new DelPhi is a C++ object‐oriented package supporting various levels of multiprocessing and memory distribution. It is demonstrated that multiprocessing results in significant improvement of computational time. Furthermore, for computations requiring large grid size (large macromolecular assemblages), the approach of memory distribution is shown to reduce the requirement of RAM and thus permitting large‐scale modeling to be done on Linux clusters with moderate architecture. The new release comes with new features, whose functionalities and applications are described as well. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.

Mutations that alter protein–DNA interactions may be pathogenic and cause diseases. Therefore, it is extremely important to quantify the effect of mutations on protein–DNA binding free energy to reveal the molecular origin of diseases and to assist the development of treatments. Although several methods that predict the change of protein–DNA binding affinity upon mutations in the binding protein were developed, the effect of DNA mutations was not considered yet. Here, we report a new version of SAMPDI, the SAMPDI-3D, which is a gradient boosting decision tree machine learning method to predict the change of the protein–DNA binding free energy caused by mutations in both the binding protein and the bases of the corresponding DNA. The method is shown to achieve Pearson correlation coefficient of 0.76 and 0.80 in a benchmarking test against experimentally determined change of the binding free energy caused by mutations in the binding protein or DNA, respectively. Furthermore, three datasets collected from literature were used to do blind benchmark for SAMPDI-3D and it is shown that it outperforms all existing state-of-the-art methods. The method is very fast allowing for genome-scale investigations. It is available as a web server and a stand-code at http://compbio.clemson.edu/SAMPDI-3D/. Supplementary data are available at Bioinformatics online.

The development of methods and algorithms to predict the effect of mutations on protein stability, protein–protein interaction, and protein–DNA/RNA binding is necessitated by the needs of protein engineering and for understanding the molecular mechanism of disease-causing variants. The vast majority of the leading methods require a database of experimentally measured folding and binding free energy changes for training. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residues on the above-mentioned thermodynamic characteristics, i.e., the mutations are not introduced at random and do not necessarily represent mutations originating from single nucleotide variants (SNV). Thus, the reported performance of the leading algorithms assessed on these databases or other limited cases may not be applicable for predicting the effect of SNVs seen in the human population. Indeed, we demonstrate that the SNVs and non-SNVs are not equally presented in the corresponding databases, and the distribution of the free energy changes is not the same. It is shown that the Pearson correlation coefficients (PCCs) of folding and binding free energy changes obtained in cases involving SNVs are smaller than for non-SNVs, indicating that caution should be used in applying them to reveal the effect of human SNVs. Furthermore, it is demonstrated that some methods are sensitive to the chemical nature of the mutations, resulting in PCCs that differ by a factor of four across chemically different mutations. All methods are found to underestimate the energy changes by roughly a factor of 2.

Electrostatics play indispensable role in practically any process in molecular biology. Indeed, at distances larger than several Angstroms all other forces are negligibly small and electrostatic force dominates. However, modeling electrostatics in molecular biology is a complicated task due to presence of water phase, mobile ions and irregularly shaped inhomogeneous biological macromolecules. A particular approach to calculating electrostatics in such systems is to apply Poisson-Boltzmann equation (PBE). Here we provide a tutorial for the popular DelPhi package that solves PBE using a finite difference method and returns the electrostatic potential distribution throughout the modeling box. The tutorial comes with detailed description of different tasks that DelPhi can handle, an assessment of the accuracy against cases with analytical solution and recommendations about DelPhi usage. Furthermore, since electrostatics is a key component of virtually any modeling in molecular biology, we have created many additional resources utilizing DelPhi to model various biology relevant quantities. Tutorials for these resources are also provided along with examples of their usage.