“…Clustering algorithms can alone identify best solutions as they tend to predominantly sample regions of the search space associated to the most native-like assembly 21 . Moreover, multi-resolution energy scoring functions can be applied to assess assembly predictions on the sole basis of intermolecular contacts 22 , as for instance using recent machine learning protocols to discriminate true protein-protein interfaces from incorrect ones 23, 24 . Eventually, final clustered solutions can be further refined using more sophisticated and computationally expensive techniques.Figure 1Assembly prediction using mViE algorithm.…”
Predicting the structure of large molecular assemblies remains a challenging task in structural biology when using integrative modeling approaches. One of the main issues stems from the treatment of heterogeneous experimental data used to predict the architecture of native complexes. We propose a new method, applied here for the first time to a set of symmetrical complexes, based on evolutionary computation that treats every available experimental input independently, bypassing the need to balance weight components assigned to aggregated fitness functions during optimization.
“…Clustering algorithms can alone identify best solutions as they tend to predominantly sample regions of the search space associated to the most native-like assembly 21 . Moreover, multi-resolution energy scoring functions can be applied to assess assembly predictions on the sole basis of intermolecular contacts 22 , as for instance using recent machine learning protocols to discriminate true protein-protein interfaces from incorrect ones 23, 24 . Eventually, final clustered solutions can be further refined using more sophisticated and computationally expensive techniques.Figure 1Assembly prediction using mViE algorithm.…”
Predicting the structure of large molecular assemblies remains a challenging task in structural biology when using integrative modeling approaches. One of the main issues stems from the treatment of heterogeneous experimental data used to predict the architecture of native complexes. We propose a new method, applied here for the first time to a set of symmetrical complexes, based on evolutionary computation that treats every available experimental input independently, bypassing the need to balance weight components assigned to aggregated fitness functions during optimization.
“…For example, protein‐protein complex structures provide atomic level details of PPIs that are useful for guiding the engineering of proteins or the rational design of therapeutic molecules to enhance or weaken specific PPI in order to achieve a desired pharmacological outcome. In this regard, computational protein docking methods offer valuable alternatives to experimental structure determination methods, especially when experimental limitations or cost considerations are present …”
Section: Introductionmentioning
confidence: 99%
“…Alternatively, a number of methods carry out local randomized searches in the context of Monte Carlo/Metropolis sampling including RosettaDock and ICM‐DISCO . Scoring functions used by these docking methods vary from empirical, knowledge‐based potentials to physical force field‐based energy functions . To date, most of the methods are based on rigid protein structures or protein models with limited flexibility.…”
Section: Introductionmentioning
confidence: 99%
“…To date, most of the methods are based on rigid protein structures or protein models with limited flexibility. In addition, desolvation contributions are often not or only empirically included in the scoring function, which may limit the accuracy of PPI prediction . Accordingly, while significant progress has been made in solving the PPI problem, there is still need for more accurate PPI prediction methods, as reflected in the quality of results from the critical assessment of predicted interactions (CAPRI) competitions that aim to promote development of new PPI prediction algorithm and optimization of the current methods.…”
Section: Introductionmentioning
confidence: 99%
“…A variety of protein-docking algorithms have been developed and evaluated during last two decades. [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] Some of these algorithms perform global searches to solve the posing problem and usually utilize fast Fourier transforms (FFT) to expedite the scoring of those generated protein poses. 9 Examples include ZDOCK, 10 FTDock, 11 PIPER,12 and GRAMM, 13 among others.…”
Protein docking methods are powerful computational tools to study protein‐protein interactions (PPI). While a significant number of docking algorithms have been developed, they are usually based on rigid protein models or with limited considerations of protein flexibility and the desolvation effect is rarely considered in docking energy functions, which may lower the accuracy of the predictions. To address these issues, we introduce a PPI energy function based on the site‐identification by ligand competitive saturation (SILCS) framework and utilize the fast Fourier transform (FFT) correlation approach. The free energy content of the SILCS FragMaps represent an alternative to traditional energy grids and they can be efficiently utilized to guide FFT‐based protein docking. Application of the approach to eight diverse test cases, including seven from Protein Docking Benchmark 5.0, showed the PPI prediction using SILCS approach (SILCS‐PPI) to be competitive with several commonly used protein docking methods indicating that the method has the ability to both qualitatively and quantitatively inform the prediction of PPI. Results show the utility of the SILCS‐PPI docking approach for determination of probability distributions of PPI interactions over the surface of both partner proteins, allowing for identification of alternate binding poses. Such binding poses are confirmed by experimental crystal contacts in our test cases. While more computationally demanding than available PPI docking technologies, we anticipate that the SILCS‐PPI docking approach will offer an alternative methodology for improved evaluation of PPIs that could be used in a variety of fields from systems biology to excipient design for biologics‐based drugs.
Since its introduction about four decades ago, docking and scoring are now the very heart of structure‐based drug design. The past 10–20 years have seen a plethora of docking and scoring tools successfully integrated into drug discovery pipelines. Interestingly, while artificial intelligence is now receiving significant attention in the computational modeling arena, docking and scoring have long utilized these techniques. This comprehensive summary highlights some of the most significant achievements of docking and scoring, reviews the current status, provides descriptions of many of the tools available, comments on some of the outstanding challenges facing the paradigm, and offers perspectives and advice on best practices for users. While significant development is still needed in docking of flexible molecules and accurate Gibb's free energy predictions, docking and scoring are very useful when handled by experienced practitioners, but less so if treated as a “black box.” Lastly, we present a hypothetical case so beginners may appreciate the nuances of setting up a docking study. Focus in docking is now shifting toward parallel applications, i.e. protein–protein, protein–oligosaccharide, protein–DNA, or protein–RNA docking and polypharmacology. In summary, this article is intended to elucidate the nuances of the subject, while providing guidelines for practical implementation of effective workflows in drug discovery and structural biology.
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