A Geometric Complementarity-Based Tool for Protein–Protein Docking

The computationally hard protein–protein complex structure prediction problem is continuously fascinating to the scientific community due to its biological impact. The field has witnessed the application of geometric algorithms, randomized algorithms, and evolutionary algorithms to name a few. These techniques improve either the searching or scoring phase. An effective searching strategy does not generate a large conformation space that perhaps demands computational power. Another determining factor is the parameter chosen for score calculation. The proposed method is an attempt to curtail the conformations by limiting the search procedure to probable regions. In this method, partial derivatives are calculated on the coarse-grained representation of the surface residues to identify the optimal points on the protein surface. Contrary to the existing geometric-based algorithms that align the convex and concave regions of both proteins, this method aligns the concave regions of the receptor with convex regions of the ligand only and thus reduces the size of conformation space. The method’s performance is evaluated using the 55 newly added targets in Protein–Protein Docking Benchmark v 5 and is found to be successful for around 47% of the targets.

Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro

It has been demonstrated earlier that the neural network based program AlphaFold2 can be used to dock proteins given the two sequences separated by a gap as the input. The protocol presented here combines AlphaFold2 with the physics based docking program ClusPro. The monomers of the model generated by AlphaFold2 are separated, re-docked using ClusPro, and the resulting 10 models are refined by AlphaFold2. Finally, the five original AlphaFold2 models are added to the 10 AlphaFold2 refined ClusPro models, and the 15 models are ranked by their predicted aligned error (PAE) values obtained by AlphaFold2. The protocol is applied to two benchmark sets of complexes, the first based on the established protein-protein docking benchmark, and the second consisting of only structures released after May 2018, the cut-off date for training AlphaFold2. It is shown that the quality of the initial AlphaFold2 models improves with each additional step of the protocol. In particular, adding the AlphaFold2 refined ClusPro models to the AlphaFold2 models increases the success rate by 23% in the top 5 predictions, whereas considering the 10 models obtained by the combined protocol increases the success rate to close to 40%. The improvement is similar for the second benchmark that includes only complexes distinct from the proteins used for training the neural network.

Dockground scoring benchmarks for protein docking

Protein docking protocols typically involve global docking scan, followed by re-ranking of the scan predictions by more accurate scoring functions that are either computationally too expensive or algorithmically impossible to include in the global scan. Development and validation of scoring methodologies are often performed on scoring benchmark sets (docking decoys) which offer concise and nonredundant representation of the global docking scan output for a large and diverse set of protein-protein complexes. Two such protein-protein scoring benchmarks were built for the Dockground resource, which contains various datasets for the development and testing of protein docking methodologies. One set was generated based on the Dockground unbound docking benchmark 4, and the other based on protein models from the Dockground model-model benchmark 2. The docking decoys were designed to reflect the reality of the real-case docking applications (e.g., correct docking predictions defined as near-native rather than native structures), and to minimize applicability of approaches not directly related to the development of scoring functions (reducing clustering of predictions in the binding funnel and disparity in structural quality of the near-native and non-native matches). The sets were further characterized by the source organism and the function of the protein-protein complexes. The sets, freely available to the research community on the Dockground webpage, present a unique, user-friendly resource for the developing and testing of protein-protein scoring approaches.

Conformational variability in proteins bound to single-stranded DNA: a new benchmark for new docking perspectives

We explored the Protein Data-Bank (PDB) to collect protein-ssDNA structures and create a multi-conformational docking benchmark including both bound and unbound protein structures. Due to ssDNA high flexibility when not bound, no ssDNA unbound structure is included. For the 143 groups identified as bound-unbound structures of the same protein , we studied the conformational changes in the protein induced by the ssDNA binding. Moreover, based on several bound or unbound protein structures in some groups, we also assessed the intrinsic conformational variability in either bound or unbound conditions, and compared it to the supposedly binding-induced modifications. This benchmark is, to our knowledge, the first attempt made to peruse available structures of protein – ssDNA interactions to such an extent, aiming to improve computational docking tools dedicated to this kind of molecular interactions.

CPdock: The Complementarity Plot for Docking of Proteins: Implementing Multi-dielectric Continuum Electrostatics

The Complementarity plot (CP) is an established validation tool for protein structures, applicable to both, globular proteins (folding) as well as protein-protein complexes (binding). It computes the shape and electrostatic complementarities (Sm, Em) for amino acid side-chains buried within the protein interior or interface and plots them in a two-dimensional plot having knowledge-based probabilistic quality estimates for the residues as well as for the whole structure. The current report essentially presents an upgraded version of the plot with the implementation of the advanced multi-dielectric functionality (as in Delphi version 6.2 or higher) in the computation of electrostatic complementarity to make the validation tool physico-chemically more realistic. The two methods (single‐ and multi-dielectric) agrees decently in their resultant Em values and hence, provisions for both methods have been kept in the software suite. So to speak, the global electrostatic balance within a well-folded protein and / or a well-packed interface seems only marginally perturbed by the choice of different internal dielectric values. However, both from theoretical as well as practical grounds, the more advanced multi-dielectric version of the plot is certainly recommended for potentially producing more reliable results. The report also presents a new methodology and a variant plot, namely, CPdock, based on the same principles of complementarity, specifically designed to be used in the docking of proteins. The efficacy of the method to discriminate between good and bad docked protein complexes have been tested on a recent state-of-the-art docking benchmark. The results unambiguously indicate that CPdock can indeed be effective in the initial screening phase of a docking scoring pipeline before going into more sophisticated and computationally expensive scoring functions. CPdock has been made available at https://github.com/nemo8130/CPdock