PyInteraph: a framework for the analysis of interaction networks in structural ensembles of proteins.

In the last years, a growing interest has been gathering around the ability of Molecular Dynamics (MD) to provide insight into the paths of long-range structural communication in biomolecules. The knowledge of the mechanisms related to structural communication helps in the rationalization in atomistic details of the effects induced by mutations, ligand binding, and the intrinsic dynamics of proteins. We here present PyInteraph, a tool for the analysis of structural ensembles inspired by graph theory.

Community-wide assessment of protein-interface modeling suggests improvements to design methodology.

The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations.

Deconvolution and database search of complex tandem mass spectra of intact proteins: a combinatorial approach.

Top-down proteomics studies intact proteins, enabling new opportunities for analyzing post-translational modifications. Because tandem mass spectra of intact proteins are very complex, spectral deconvolution (grouping peaks into isotopomer envelopes) is a key initial stage for their interpretation. In such spectra, isotopomer envelopes of different protein fragments span overlapping regions on the m/z axis and even share spectral peaks.

Protein structure modelling and evaluation based on a 4-distance description of side-chain interactions.

Background: Accurate evaluation and modelling of residue-residue interactions within and between proteins is a key aspect of computational structure prediction including homology modelling, protein-protein docking, refinement of low-resolution structures, and computational protein design.

Results: Here we introduce a method for accurate protein structure modelling and evaluation based on a novel 4-distance description of residue-residue interaction geometry. Statistical 4-distance preferences were extracted from high-resolution protein structures and were used as a basis for a knowledge-based potential, called Hunter.

Novel approach for efficient pharmacophore-based virtual screening: method and applications.

Virtual screening is emerging as a productive and cost-effective technology in rational drug design for the identification of novel lead compounds. An important model for virtual screening is the pharmacophore. Pharmacophore is the spatial configuration of essential features that enable a ligand molecule to interact with a specific target receptor.

Deterministic pharmacophore detection via multiple flexible alignment of drug-like molecules.

We present a novel highly efficient method for the detection of a pharmacophore from a set of drug-like ligands that interact with a target receptor. A pharmacophore is a spatial arrangement of physico-chemical features in a ligand that is essential for the interaction with a specific receptor. In the absence of a known three-dimensional (3D) receptor structure, a pharmacophore can be identified from a multiple structural alignment of ligand molecules.

PharmaGist: a webserver for ligand-based pharmacophore detection.

Predicting molecular interactions is a major goal in rational drug design. Pharmacophore, which is the spatial arrangement of features that is essential for a molecule to interact with a specific target receptor, is an important model for achieving this goal. We present a freely available web server, named PharmaGist, for pharmacophore detection.

Mechanism of two classes of cancer mutations in the phosphoinositide 3-kinase catalytic subunit.

Many human cancers involve up-regulation of the phosphoinositide 3-kinase PI3Kalpha, with oncogenic mutations identified in both the p110alpha catalytic and the p85alpha regulatory subunits. We used crystallographic and biochemical approaches to gain insight into activating mutations in two noncatalytic p110alpha domains-the adaptor-binding and the helical domains. A structure of the adaptor-binding domain of p110alpha in a complex with the p85alpha inter-Src homology 2 (inter-SH2) domain shows that oncogenic mutations in the adaptor-binding domain are not at the inter-SH2 interface but in a polar surface patch that is a plausible docking site for other domains in the holo p110/p85 complex.

A hierarchical protein folding scheme based on the building block folding model.

The building block protein folding model states that the native protein structure is the product of a combinatorial assembly of relatively structurally independent contiguous parts of the protein that possess a hydrophobic core, i.e., building blocks (BBs).

Combinatorial docking approach for structure prediction of large proteins and multi-molecular assemblies.

Protein folding and protein binding are similar processes. In both, structural units combinatorially associate with each other. In the case of folding, we mostly handle relatively small units, building blocks or domains, that are covalently linked.

Geometry-based flexible and symmetric protein docking.

We present a set of geometric docking algorithms for rigid, flexible, and cyclic symmetry docking. The algorithms are highly efficient and have demonstrated very good performance in CAPRI Rounds 3-5. The flexible docking algorithm, FlexDock, is unique in its ability to handle any number of hinges in the flexible molecule, without degradation in run-time performance, as compared to rigid docking.

Approaching the CAPRI challenge with an efficient geometry-based docking.

The last 3 rounds (3-5) of CAPRI included a wide range of docking targets. Several targets were especially challenging, since they involved large-scale movements and symmetric rearrangement, while others were based on homology models. We have approached the targets with a variety of geometry-based docking algorithms that include rigid docking, symmetric docking, and flexible docking with symmetry constraints.

PatchDock and SymmDock: servers for rigid and symmetric docking.

Here, we describe two freely available web servers for molecular docking. The PatchDock method performs structure prediction of protein-protein and protein-small molecule complexes. The SymmDock method predicts the structure of a homomultimer with cyclic symmetry given the structure of the monomeric unit.

Prediction of multimolecular assemblies by multiple docking.

The majority of proteins function when associated in multimolecular assemblies. Yet, prediction of the structures of multimolecular complexes has largely not been addressed, probably due to the magnitude of the combinatorial complexity of the problem. Docking applications have traditionally been used to predict pairwise interactions between molecules.

Protein structure prediction via combinatorial assembly of sub-structural units.

Following the hierarchical nature of protein folding, we propose a three-stage scheme for the prediction of a protein structure from its sequence. First, the sequence is cut to fragments that are each assigned a structure. Second, the assigned structures are combinatorially assembled to form the overall 3D organization.

Taking geometry to its edge: fast unbound rigid (and hinge-bent) docking.

We present a very efficient rigid "unbound" soft docking methodology, which is based on detection of geometric shape complementarity, allowing liberal steric clash at the interface. The method is based on local shape feature matching, avoiding the exhaustive search of the 6D transformation space. Our experiments at CAPRI rounds 1 and 2 show that although the method does not perform an exhaustive search of the 6D transformation space, the "correct" solution is never lost.