Kinase Inhibitor Profile For Human Nek1, Nek6, And Nek7 And Analysis Of The Structural Basis For Inhibitor Specificity.

Human Neks are a conserved protein kinase family related to cell cycle progression and cell division and are considered potential drug targets for the treatment of cancer and other pathologies. We screened the activation loop mutant kinases hNek1 and hNek2, wild-type hNek7, and five hNek6 variants in different activation/phosphorylation statesand compared them against 85 compounds using thermal shift denaturation. We identified three compounds with significant Tm shifts: JNK Inhibitor II for hNek1(Δ262-1258)-(T162A), Isogranulatimide for hNek6(S206A), andGSK-3 Inhibitor XIII for hNek7wt. Each one of these compounds was also validated by reducing the kinases activity by at least 25%. The binding sites for these compounds were identified by in silico docking at the ATP-binding site of the respective hNeks. Potential inhibitors were first screened by thermal shift assays, had their efficiency tested by a kinase assay, and were finally analyzed by molecular docking. Our findings corroborate the idea of ATP-competitive inhibition for hNek1 and hNek6 and suggest a novel non-competitive inhibition for hNek7 in regard to GSK-3 Inhibitor XIII. Our results demonstrate that our approach is useful for finding promising general and specific hNekscandidate inhibitors, which may also function as scaffolds to design more potent and selective inhibitors.201176-9

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

Implementation of a hybrid approach using comparative and ab initio modelling to predict the three dimensional structure of proteins containing multiple domains and flexible connectors

Domínio proteico é uma sequência de aminoácidos evolutivamente conservada e funcionalmente independente. Um dos aspectos mais importantes do estudo de uma proteína que contem múltiplos domínios é o entendimento da comunicação, entre os diferentes domínios, e seu papel biológico. Essa comunicação em maior parte é feita pela interação direta entre domínios. A interação poderia ser tratada como uma clássica interação proteína-proteína. Entretanto, proteínas multidomínio possuem restrições determinadas por suas regiões conectoras. Os conectores interdomínio impõem restrições e limitam espaço conformacional dos domínios. Apresentamos aqui o MAD, uma rotina capaz de obter modelos tridimensionais de alta resolução para proteínas, contendo qualquer número de domínios, a partir de sua sequencia primária. Os domínios conservados são identificados utilizando a base de domínios conservados (CDD) e seus limites são utilizados para definir as regiões conectoras. É criado um ensamble de possíveis dobramentos dos conectores e sua distribuição de distâncias C/N-terminais são utilizadas como restrição espacial na busca pela interação entre os domínios.Os modelos dos domínios são obtidos por uma modelagem comparativa. Foi implementada uma heurística, capaz de lidar com a natureza combinatorial dos múltiplos domínios e com a necessidade imposta pela limitação computacional de realizar o docking dos domínios em forma de pares. Todas combinações de domínios são submetidas as rotinas de docking. Aplica-se filtro de distância e energético, excluindo as conformações que apresentam distância C/N-terminal entre domínios maior do que o valor máximo observado no ensamble de conectores e seleciona as conformações energeticamente mais favoráveis. As conformações são submetidas a uma rotina de agrupamento hierárquico baseada em sua similaridade estrutural. Para a segunda fase as conformações selecionadas são pareadas com seu domínio complementar e ressubmetidas a rotina de docking até que todas as fases tenham sido completadas. Foi criado um conjunto de testes a partir do Protein Data Bank contendo 54 proteínas multidomínio para que a rotina de docking do MAD fosse comparada com outros softwares utilizados pela comunidade cientifica, mostrou-se superior ou equivalente aos métodos testados. A capacidade de utilizar dados experimentais foi demostrada através da proposição de um modelo da forma ativa da enzima tirosina fosfatase 2, nunca observado experimentalmente. A rotina de docking foi expandida paralelamente em uma aplicação standalone e utilizada na resolução de diversos problemas biológicos. Concluímos que a inovação metodológica proposta pelo MAD é de grande valia para a modelagem molecular e tem potencial de gerar uma nova perspectiva a respeito da interação de proteína multidomínio, visto que é possível analisar essas proteínas em sua plenitude e não como domínios separados.Protein domain is an evolutionary conserved and functionally independent amino acid sequence. One of the most important aspects of the study of a protein that contains multiple domains is the understanding of communication between the different areas, and their biological role. This communication is made mostly by direct interaction between domains. The interaction could be treated as a classical protein-protein interaction. However, multidomain proteins have certain restrictions for its connector regions. The intra connectors impose restrictions and limit conformational space of the domains. We present the MAD, a routine able to get three-dimensional models of high-resolution protein, containing any number of domains, from its primary sequence. The conserved domains are identified using the basic conserved domains database (CDD) and its boundaries are used to define the connector regions. This creates a ensemble of possible folding of the connectors and distribution of distances C/N-terminals are used as spatial restriction in the search for interaction between domains.Os models of the domains are obtained by comparative modelling. A heuristic able to handle the combinatorial nature of the multiple areas and the need imposed by the computer to perform the limitation of the docking areas as pairs was implemented. All combinations of domains are referred to the docking routines. Distance and energy filters are applied, excluding conformations that have C/N-terminal domains distances larger than the maximum value observed in the connectors ensemble and selects the most favourable energy conformations. Conformations are subjected to hierarchical clustering routine based on their structural similarity. For the second phase, the selected conformations are paired with its complementary domain and resubmitted to the docking routine until all phases have been completed. A test set has been created from the Protein Data Bank containing 54 multidomain proteins so that the docking routine of MAD could be compared with other software used by the scientific community, it has been shown to be superior or equivalent to the tested methods. The ability to use experimental data was demonstrated by proposing a model of the active form of tyrosine phosphatase enzyme 2, never observed experimentally. The docking routine was expanded in a standalone application and used in solving various biological problems. We conclude that the methodological innovation proposed by the MAD is very useful for molecular modelling and has the potential to generate a new perspective on multidomain protein interaction as you can analyse these proteins in its entirety and not as separate domains

Less is more: Coarse-grained integrative modeling of large biomolecular assemblies with HADDOCK

Predicting the 3D structure of protein interactions remains a challenge in the field of computational structural biology. This is in part due to difficulties in sampling the complex energy landscape of multiple interacting flexible polypeptide chains. Coarse-graining approaches, which reduce the number of degrees of freedom of the system, help address this limitation by smoothing the energy landscape, allowing an easier identification of the global energy minimum. They also accelerate the calculations, allowing to model larger assemblies. Here, we present the implementation of the MARTINI coarse-grained force field for proteins into HADDOCK, our integrative modelling platform. Docking and refinement are performed at the coarse-grained level and the resulting models are then converted back to atomistic resolution through a distance restraints-guided morphing procedure. Our protocol, tested on the largest complexes of the protein docking benchmark 5, shows an overall ~7-fold speed increase compared to standard all-atom calculations, while maintaining a similar accuracy and yielding substantially more near-native solutions. To showcase the potential of our method, we performed simultaneous 7 body docking to model the 1:6 KaiC-KaiB complex, integrating mutagenesis and hydrogen/deuterium exchange data from mass spectrometry with symmetry restraints, and validated the resulting models against a recently published cryo-EM structure

Structural insights regarding an insecticidal Talisia esculenta protein and its biotechnological potential for Diatraea saccharalis larval control

Talisin is a seed-storage protein from Talisia esculenta that presents lectin-like activities, as well as proteinase-inhibitor properties. The present study aims to provide new in vitro and in silico biochemical information about this protein, shedding some light on its mechanistic inhibitory strategies. A theoretical three-dimensional structure of Talisin bound to trypsin was constructed in order to determine the relative interaction mode. Since the structure of non-competitive inhibition has not been elucidated, Talisin-trypsin docking was carried out using Hex v5.1, since the structure of non-competitive inhibition has not been elucidated. The predicted non-coincidence of the trypsin binding site is completely different from that previously proposed for Kunitz-type inhibitors, which demonstrate a substitution of an Arg(64) for the Glu(64) residue. Data, therefore, provide more information regarding the mechanisms of non-competitive plant proteinase inhibitors. Bioassays with Talisin also presented a strong insecticide effect on the larval development of Diatraea saccharalis, demonstrating LD50 and ED50 of ca. 2.0% and 1.5%, respectively. (C) 2011 Elsevier Inc. All rights reserved.FUNDECT (Fundacao de Apoio ao Desenvolvimento do Ensino, Ciencia e Tecnologia do Estado de Mato Grosso do Sul)FUNDECT (Fundacao de Apoio ao Desenvolvimento do Ensino, Ciencia e Tecnologia do Estado de Mato Grosso do Sul)CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico)Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)FINEP (Ministerio da Ciencia e Tecnologia)FINEP (Ministerio da Ciencia e Tecnologia

Molecular dynamics simulations in drug discovery and pharmaceutical development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application pos-sibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substan-tially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies

Structural Analysis of Intermolecular Interactions in the Kinesin Adaptor Complex Fasciculation and Elongation Protein Zeta 1/ Short Coiled-Coil Protein (FEZ1/SCOCO)

<div><p>Cytoskeleton and protein trafficking processes, including vesicle transport to synapses, are key processes in neuronal differentiation and axon outgrowth. The human protein FEZ1 (fasciculation and elongation protein zeta 1 / UNC-76, in <i>C. elegans</i>), SCOCO (short coiled-coil protein / UNC-69) and kinesins (e.g. kinesin heavy chain / UNC116) are involved in these processes. Exploiting the feature of FEZ1 protein as a bivalent adapter of transport mediated by kinesins and FEZ1 protein interaction with SCOCO (proteins involved in the same path of axonal growth), we investigated the structural aspects of intermolecular interactions involved in this complex formation by NMR (Nuclear Magnetic Resonance), cross-linking coupled with mass spectrometry (MS), SAXS (Small Angle X-ray Scattering) and molecular modelling. The topology of homodimerization was accessed through NMR (Nuclear Magnetic Resonance) studies of the region involved in this process, corresponding to FEZ1 (92-194). Through studies involving the protein in its monomeric configuration (reduced) and dimeric state, we propose that homodimerization occurs with FEZ1 chains oriented in an anti-parallel topology. We demonstrate that the interaction interface of FEZ1 and SCOCO defined by MS and computational modelling is in accordance with that previously demonstrated for UNC-76 and UNC-69. SAXS and literature data support a heterotetrameric complex model. These data provide details about the interaction interfaces probably involved in the transport machinery assembly and open perspectives to understand and interfere in this assembly and its involvement in neuronal differentiation and axon outgrowth.</p> </div

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies

Interaction between FEZ1 and SCOCO.

<p>A) Purified recombinant proteins FEZ1 and SCOCO were incubated, chemically cross-linked, digested with trypsin, and analyzed by MS. MS/MS spectra were manually validated for b and y ion series of the α (peptide of FEZ1) and β (peptide of SCOCO) chains. B) General scheme of FEZ1 and SCOCO proteins cross-linked. Coiled-coils: box, alpha-helix prediction: gray. Amino acids 261-279 in FEZ1 correspond to the mininal interaction region of UNC-69/SCOCO in UNC-76/FEZ1.C) Best conformation based on both cross-link distance and energy value of the in silico modeled complex. FEZ1 is colored in green, and SCOCO is depicted in deep blue. The peptides identified in the MS analysis are shown in orange and the lysine residue in red. DSS is represented in yellow.</p