An overview of data‐driven HADDOCK strategies in CAPRI rounds 38-45

Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38-45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement/CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher-quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models

A novel antifolate suppresses growth of FPGS-deficient cells and overcomes methotrexate resistance

Cancer cells make extensive use of the folate cycle to sustain increased anabolic metabolism. Multiple chemotherapeutic drugs interfere with the folate cycle, including methotrexate and 5-fluorouracil that are commonly applied for the treatment of leukemia and colorectal cancer (CRC), respectively. Despite high success rates, therapy-induced resistance causes relapse at later disease stages. Depletion of folylpolyglutamate synthetase (FPGS), which normally promotes intracellular accumulation and activity of natural folates and methotrexate, is linked to methotrexate and 5-fluorouracil resistance and its association with relapse illustrates the need for improved intervention strategies. Here, we describe a novel antifolate (C1) that, like methotrexate, potently inhibits dihydrofolate reductase and downstream one-carbon metabolism. Contrary to methotrexate, C1 displays optimal efficacy in FPGS-deficient contexts, due to decreased competition with intracellular folates for interaction with dihydrofolate reductase. We show that FPGS-deficient patient-derived CRC organoids display enhanced sensitivity to C1, whereas FPGS-high CRC organoids are more sensitive to methotrexate. Our results argue that polyglutamylation-independent antifolates can be applied to exert selective pressure on FPGS-deficient cells during chemotherapy, using a vulnerability created by polyglutamylation deficiency

A novel antifolate suppresses growth of FPGS-deficient cells and overcomes methotrexate resistance

Cancer cells make extensive use of the folate cycle to sustain increased anabolic metabolism. Multiple chemotherapeutic drugs interfere with the folate cycle, including methotrexate and 5-fluorouracil that are commonly applied for the treatment of leukemia and colorectal cancer (CRC), respectively. Despite high success rates, therapy-induced resistance causes relapse at later disease stages. Depletion of folylpolyglutamate synthetase (FPGS), which normally promotes intracellular accumulation and activity of natural folates and methotrexate, is linked to methotrexate and 5-fluorouracil resistance and its association with relapse illustrates the need for improved intervention strategies. Here, we describe a novel antifolate (C1) that, like methotrexate, potently inhibits dihydrofolate reductase and downstream one-carbon metabolism. Contrary to methotrexate, C1 displays optimal efficacy in FPGS-deficient contexts, due to decreased competition with intracellular folates for interaction with dihydrofolate reductase. We show that FPGS-deficient patient-derived CRC organoids display enhanced sensitivity to C1, whereas FPGS-high CRC organoids are more sensitive to methotrexate. Our results argue that polyglutamylation-independent antifolates can be applied to exert selective pressure on FPGS-deficient cells during chemotherapy, using a vulnerability created by polyglutamylation deficiency

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.Cancer Research UK, Grant/Award Number: FC001003; Changzhou Science and Technology Bureau, Grant/Award Number: CE20200503; Department of Energy and Climate Change, Grant/Award Numbers: DE-AR001213, DE-SC0020400, DE-SC0021303; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Institut national de recherche en informatique et en automatique (INRIA), Grant/Award Number: Cordi-S; Lietuvos Mokslo Taryba, Grant/Award Numbers: S-MIP-17-60, S-MIP-21-35; Medical Research Council, Grant/Award Number: FC001003; Japan Society for the Promotion of Science KAKENHI, Grant/Award Number: JP19J00950; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-110167RB-I00; Narodowe Centrum Nauki, Grant/Award Numbers: UMO-2017/25/B/ST4/01026, UMO-2017/26/M/ST4/00044, UMO-2017/27/B/ST4/00926; National Institute of General Medical Sciences, Grant/Award Numbers: R21GM127952, R35GM118078, RM1135136, T32GM132024; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM078221, R01GM093123, R01GM109980, R01GM133840, R01GN123055, R01HL142301, R35GM124952, R35GM136409; National Natural Science Foundation of China, Grant/Award Number: 81603152; National Science Foundation, Grant/Award Numbers: AF1645512, CCF1943008, CMMI1825941, DBI1759277, DBI1759934, DBI1917263, DBI20036350, IIS1763246, MCB1925643; NWO, Grant/Award Number: TOP-PUNT 718.015.001; Wellcome Trust, Grant/Award Number: FC00100

Integrative Modelling of Biomolecular Complexes: From Small to Large

Chapter 1 provided a detailed and comprehensive review on the types of data than can be used by Integrative Modelling software like HADDOCK, ROSETTA and IMP, with a particular emphasis on the experimental techniques which can be used to map interfaces, derive distance restraints or shape-based approaches. Another focal point of the chapter is how recent advancements have affected the field of membrane protein modelling. Chapters 2 and 3 also relate to membrane protein modelling with the former describing a recently available benchmark comprised of ready-to-dock membrane protein complexes and the baseline performance of HADDOCK for the entries of the benchmark, and the latter, ongoing work regarding development of a protocol for HADDOCK for the docking of transmembrane protein complexes. The remaining of the thesis focused on small molecule modelling with Chapters 4-6 detailing three separate protocols for the docking of small molecules and protein receptors, with every protocol and chapter reflecting methodological improvements over the previous one. In Chapter 4, I described the participation of HADDOCK in the 2016 iteration of the Grand Challenge, the blind docking experiment organised by the D3R consortium. While our performance in the pose prediction component was not impressive, we could identify the main factor limiting HADDOCK’s performance, namely the selection of appropriate templates for the receptor and came up with an improved way of selecting receptors. Chapter 5 described additional improvements in our protocol related to the way the compound conformers are selected prior to docking which led to our participation in the 2017 iteration of the Grand Challenge being evaluated as one of the best. Chapter 6 detailed the development of a new protocol for protein-small molecule docking, by combining the lessons and conclusions from Chapters 4-5 and formalising their approaches in a method that relies on HADDOCK’s main strength, its ability to incorporate information to guide the simulation. This new, shape-restrained docking protocol outperformed all our previous efforts while at the same time not relying on any external software. A common denominator between the membrane protein work and the small ligand docking discussed in this thesis is the use of shape information. Indeed, chapters 3 and 6 both describe applications of shape information represented as beads to drive the modelling process. In Chapter 3 one or more layers of beads are used to implicitly represent the membrane and in Chapter 6 ligand docking is restrained to a shape based on the structure of a homologous compound. Despite the commonalities between the two protocols, the outcome of the docking is very different between the two, with the small molecule protocol achieving high-quality results and improving upon our previous efforts in this area, whereas the membrane one achieves results which are only marginally better than defining centre-of-mass restraints between the transmembrane segments of the partners for the docking. A main limiting factor in the case of membrane protein complexes seems to be the size of the complex, which defines the number of restraints defined between shape and molecules and negatively impacts performance

Integrative Modelling of Biomolecular Complexes: From Small to Large

Chapter 1 provided a detailed and comprehensive review on the types of data than can be used by Integrative Modelling software like HADDOCK, ROSETTA and IMP, with a particular emphasis on the experimental techniques which can be used to map interfaces, derive distance restraints or shape-based approaches. Another focal point of the chapter is how recent advancements have affected the field of membrane protein modelling. Chapters 2 and 3 also relate to membrane protein modelling with the former describing a recently available benchmark comprised of ready-to-dock membrane protein complexes and the baseline performance of HADDOCK for the entries of the benchmark, and the latter, ongoing work regarding development of a protocol for HADDOCK for the docking of transmembrane protein complexes. The remaining of the thesis focused on small molecule modelling with Chapters 4-6 detailing three separate protocols for the docking of small molecules and protein receptors, with every protocol and chapter reflecting methodological improvements over the previous one. In Chapter 4, I described the participation of HADDOCK in the 2016 iteration of the Grand Challenge, the blind docking experiment organised by the D3R consortium. While our performance in the pose prediction component was not impressive, we could identify the main factor limiting HADDOCK’s performance, namely the selection of appropriate templates for the receptor and came up with an improved way of selecting receptors. Chapter 5 described additional improvements in our protocol related to the way the compound conformers are selected prior to docking which led to our participation in the 2017 iteration of the Grand Challenge being evaluated as one of the best. Chapter 6 detailed the development of a new protocol for protein-small molecule docking, by combining the lessons and conclusions from Chapters 4-5 and formalising their approaches in a method that relies on HADDOCK’s main strength, its ability to incorporate information to guide the simulation. This new, shape-restrained docking protocol outperformed all our previous efforts while at the same time not relying on any external software. A common denominator between the membrane protein work and the small ligand docking discussed in this thesis is the use of shape information. Indeed, chapters 3 and 6 both describe applications of shape information represented as beads to drive the modelling process. In Chapter 3 one or more layers of beads are used to implicitly represent the membrane and in Chapter 6 ligand docking is restrained to a shape based on the structure of a homologous compound. Despite the commonalities between the two protocols, the outcome of the docking is very different between the two, with the small molecule protocol achieving high-quality results and improving upon our previous efforts in this area, whereas the membrane one achieves results which are only marginally better than defining centre-of-mass restraints between the transmembrane segments of the partners for the docking. A main limiting factor in the case of membrane protein complexes seems to be the size of the complex, which defines the number of restraints defined between shape and molecules and negatively impacts performance