Network theory methods are being increasingly applied to proteins to investigate complex biological phenomena. Residues that are important for signaling processes can be identified by their condition as critical nodes in a protein structure network. This analysis involves modeling the protein as a graph in which each residue is represented as a node and edges are drawn between nodes that are deemed connected. In this paper, we show that the results obtained from this type of network analysis (i.e., signaling pathways, key residues for signal transmission, etc.) are profoundly affected by the topology of the network, with normally used determination of network edges by geometrical cutoff schemes giving rise to substantial statistical errors. We propose a method of determining protein structure networks by calculating inter-residue interaction energies and show that it gives an accurate and reliable description of the signal-propagation properties of a known allosteric enzyme. We also show that including covalent interactions in the network topology is essential for accurate results to be obtained.

A mecânica molecular é amplamente usada na simulação de biomoléculas. Um arquivo de topologia molecular contendo todos os tipos de átomos, que dependem do ambiente químico, tem de ser construído. MKTOP é um software gratuito capaz de construir topologias moleculares para o GROMACS, sendo assim uma ferramenta útil para seus usuários.Molecular mechanics methods are widely-used for simulations of biomolecules. A molecular topology file containing all atom types, which depend on the chemical environment, must be constructed. MKTOP is a free-software capable of automatic atom type recognition and construction of molecular topologies for GROMACS, being a useful tool for its users. Keywords: molecular mechanics, atom-type recognition, molecular topology, GROMACS, OPLS IntroductionThe use of molecular mechanics (MM) methods in the treatment of biomolecular systems has been increasing steadily. These methods use empirical force fields to calculate interactions between atoms. Applications range from simple geometry optimizations to estimations of binding affinity, molecular dynamics (MD) simulations, among others. GROMACS 1 is one of the most utilized MD software packages and it has the advantage of being free software, distributed under the GNU license. It is able to perform calculations with several force fields, one of them being OPLS-AA.2 This combination is widely-used in protein simulations.In order to set up a MM calculation, one must generate a topology file for the system, in which the connectivities between the atoms and the atom type, which is determined by the concept of chemical function, are specified.Chemical function determination is the major problem to address when creating a topology. Most molecular mechanics softwares have routines for generating a topology, but these are usually only effective for peptides. Although the systems of main interest in the field are proteins, possible inhibitors, co-factors, substrates and agonists may not be peptides. Therefore, automatic routines which successfully identify atom types are extremely helpful to the user. One such routine is called PRODRG 3 , but it is only able to create topologies for united-atom force fields. More recently, a routine named antechamber, 4 capable of creating all-atom molecular topologies, has been included in the last AMBER 5 distribution. However, AMBER is not freely available. Therefore, a program capable of building topologies for GROMACS would be of great appeal.In this paper we report the development of an automatic routine, named MKTOP, to create molecular topologies for GROMACS, utilizing the OPLS-AA force-field. Computational MethodsMKTOP is a user-friendly computational routine written in Perl. A single command line is required to generate a molecular topology. How MKTOP worksThe starting point for constructing a topology is, obviously, a list of the coordinates of all the atoms present in the system; these must be provided to MKTOP in a PDB format file. The atomic charges, which are essential to perform a MM calculation, may be ...

Much work has been done in the past decade to quantify the phenomenon of allosteric communication in proteins. Every new study unveils an extra piece of the puzzle in our search for an understanding of allostery that allows us to make predictions on the response of a protein to medically relevant stimuli such as pathological mutations or drug binding. This review summarizes recent advances in the analysis of mechanisms of allosteric communication in proteins, and combines this new knowledge to offer a perspective of allostery which is consistent with chemical views of molecular processes. First, we review recent work, particularly computational, on the characterization of signal propagation and conformational changes in allosteric proteins. We then compare different models of allostery, and discuss the significance of the concept of an allosteric pathway. We argue that allostery can be rationalized in terms of pathways of residues that efficiently transmit energy between different binding sites. We then provide examples that show how this picture could account for most of the observed data, since energy flow may be manifested as changes in both structure and dynamics. We conclude by acknowledging that the proposed view is still a simplification and should not be taken as a rigorous model of allosteric communication in proteins. Nevertheless, simple pictures like this can go a long way in improving our understanding of many complex phenomena observed in nature.

Understanding how allosteric proteins respond to changes in their environment is a major goal of current biological research. We show that these responses can be quantified by analyzing protein energy networks using a method recently developed in our group. On the basis of this method, we introduce here a quantity named energetic coupling, which we show is able to discriminate allosterically active mutants of the lactose repressor (LacI) protein, and of the catabolite activator protein (CAP), a dynamically driven allosteric protein. Our method assumes that allostery and signal transmission can be more accurately described as efficient energy propagation, and not as the more widely used atomic motion correlations. We demonstrate the validity of this assumption by performing energy-propagation simulations. Finally, we present results from energy-propagation simulations performed on folded and fully extended conformations of the postsynaptic density protein 95 (PSD-95). They show that the protein backbone provides a more efficient route for energy transfer, when compared to secondary or tertiary contacts. On the basis of this, we propose energy propagation through the backbone as a possible explanation for the observation that intrinsically disordered proteins can efficiently transmit signals while lacking a well-defined tertiary structure.

Diamond STING is a new version of the STING suite of programs for a comprehensive analysis of a relationship between protein sequence, structure, function and stability. We have added a number of new functionalities by both providing more structure parameters to the STING Database and by improving/expanding the interface for enhanced data handling. The integration among the STING components has also been improved. A new key feature is the ability of the STING server to handle local files containing protein structures (either modeled or not yet deposited to the Protein Data Bank) so that they can be used by the principal STING components: JavaProtein Dossier (JPD) and STING Report. The current capabilities of the new STING version and a couple of biologically relevant applications are described here. We have provided an example where Diamond STING identifies the active site amino acids and folding essential amino acids (both previously determined by experiments) by filtering out all but those residues by selecting the numerical values/ranges for a set of corresponding parameters. This is the fundamental step toward a more interesting endeavor—the prediction of such residues. Diamond STING is freely accessible at and .