Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set.

Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)-ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across receptor binding domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP). These predictors were selected based on their user-friendliness, accessibility, and speed. As a result of our benchmarking efforts, we observed that none of the methods could generate a meaningful correlation with the experimental binding data. The best correlation is achieved by FoldX (R = -0.51). When we simplified the prediction problem to a binary classification, that is, whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with a 64% success rate. Surprisingly, on this set, simple energetic scoring functions performed significantly better than the ones using extra evolutionary-based terms, as in Mutabind and SSIPe. Furthermore, we demonstrated that recent AI approaches, mmCSM-PPI and TopNetTree, yielded comparable performances to the force field-based techniques. These observations suggest plenty of room to improve the binding affinity predictors in guessing the variant-induced binding profile changes of a host-pathogen system, such as Spike-ACE2. To aid such improvements we provide our benchmarking data at https://github.com/CSB-KaracaLab/RBD-ACE2-MutBench with the option to visualize our mutant models at https://rbd-ace2-mutbench.github.io/.

Dynamic proximity interaction profiling suggests that YPEL2 is involved in cellular stress surveillance.

YPEL2 is a member of the evolutionarily conserved YPEL family involved in cellular proliferation, mobility, differentiation, senescence, and death. However, the mechanism by which YPEL2, or YPEL proteins, mediates its effects is largely unknown. Proteins perform their functions in a network of proteins whose identities, amounts, and compositions change spatiotemporally in a lineage-specific manner in response to internal and external stimuli. Here, we explored interaction partners of YPEL2 by using dynamic TurboID-coupled mass spectrometry analyses to infer a function for the protein. Our results using inducible transgene expressions in COS7 cells indicate that proximity interaction partners of YPEL2 are mainly involved in RNA and mRNA metabolic processes, ribonucleoprotein complex biogenesis, regulation of gene silencing by miRNA, and cellular responses to stress. We showed that YPEL2 interacts with the RNA-binding protein ELAVL1 and the selective autophagy receptor SQSTM1. We also found that YPEL2 localizes stress granules in response to sodium arsenite, an oxidative stress inducer, which suggests that YPEL2 participates in stress granule-related processes. Establishing a point of departure in the delineation of structural/functional features of YPEL2, our results suggest that YPEL2 may be involved in stress surveillance mechanisms.

Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15.

For the first time, the 2022 CASP (Critical Assessment of Structure Prediction) community experiment included a section on computing multiple conformations for protein and RNA structures. There was full or partial success in reproducing the ensembles for four of the nine targets, an encouraging result. For protein structures, enhanced sampling with variations of the AlphaFold2 deep learning method was by far the most effective approach. One substantial conformational change caused by a single mutation across a complex interface was accurately reproduced. In two other assembly modeling cases, methods succeeded in sampling conformations near to the experimental ones even though environmental factors were not included in the calculations. An experimentally derived flexibility ensemble allowed a single accurate RNA structure model to be identified. Difficulties included how to handle sparse or low-resolution experimental data and the current lack of effective methods for modeling RNA/protein complexes. However, these and other obstacles appear addressable.

The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15.

In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases, the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas, and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes also remains challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved a 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

An alternatively spliced PD-L1 isoform PD-L1∆3, and PD-L2 expression in breast cancers: implications for eligibility scoring and immunotherapy response.

Targeting PD-1/PD-L1 has shown substantial therapeutic response and unprecedented long-term durable responses in the clinic. However, several challenges persist, encompassing the prediction of treatment effectiveness and patient responses, the emergence of treatment resistance, and the necessity for additional biomarkers. Consequently, we comprehensively explored the often-overlooked isoforms of crucial immunotherapy players, leveraging transcriptomic analysis, structural modeling, and immunohistochemistry (IHC) data. Our investigation has led to the identification of an alternatively spliced isoform of PD-L1 that lacks exon 3 (PD-L1∆3) and the IgV domain required to interact with PD-1. PD-L1∆3 is expressed more than the canonical isoform in a subset of breast cancers and other TCGA tumors. Using the deep learning-based protein modeling tool AlphaFold2, we show the lack of a possible interaction between PD-L1∆3 and PD-1. In addition, we present data on the expression of an additional ligand for PD-1, PD-L2. PD-L2 expression is widespread and positively correlates with PD-L1 levels in breast and other tumors. We report enriched epithelial-mesenchymal transition (EMT) signature in high PD-L2 transcript expressing (PD-L2 > PD-L1) tumors in all breast cancer subtypes, highlighting potential crosstalk between EMT and immune evasion. Notably, the estrogen gene signature is downregulated in ER + breast tumors with high PD-L2. The data on PD-L2 IHC positivity but PD-L1 negativity in breast tumors, together with our results on PD-L1∆3, highlight the need to utilize PD-L2 and PD-L1 isoform-specific antibodies for staining patient tissue sections to offer a more precise prediction of the outcomes of PD-1/PD-L1 immunotherapy.

Cryogenic X-ray crystallographic studies of biomacromolecules at Turkish Light Source "Turkish DeLight".

X-ray crystallography is a robust and powerful structural biology technique that provides high-resolution atomic structures of biomacromolecules. Scientists use this technique to unravel mechanistic and structural details of biological macromolecules (e.g., proteins, nucleic acids, protein complexes, protein-nucleic acid complexes, or large biological compartments). Since its inception, single-crystal cryocrystallography has never been performed in Türkiye due to the lack of a single-crystal X-ray diffractometer. The X-ray diffraction facility recently established at the University of Health Sciences, Istanbul, Türkiye will enable Turkish and international researchers to easily perform high-resolution structural analysis of biomacromolecules from single crystals. Here, we describe the technical and practical outlook of a state-of-the-art home-source X-ray, using lysozyme as a model protein. The methods and practice described in this article can be applied to any biological sample for structural studies. Therefore, this article will be a valuable practical guide from sample preparation to data analysis.

The Impact of AI-Based Modeling on the Accuracy of Protein Assembly Prediction: Insights from CASP15.

In CASP15, 87 predictors submitted around 11,000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact prediction, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes remains also challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved the 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

Molecular Mechanism of Nucleosome Recognition by the Pioneer Transcription Factor Sox.

Pioneer transcription factors (PTFs) have the remarkable ability to directly bind to chromatin to stimulate vital cellular processes. In this work, we dissect the universal binding mode of Sox PTF by combining extensive molecular simulations and physiochemistry approaches, along with DNA footprinting techniques. As a result, we show that when Sox consensus DNA is located at the solvent-facing DNA strand, Sox binds to the compact nucleosome without imposing any significant conformational changes. We also reveal that the base-specific Sox:DNA interactions (base reading) and Sox-induced DNA changes (shape reading) are concurrently required for sequence-specific nucleosomal DNA recognition. Among three different nucleosome positions located on the positive DNA arm, a sequence-specific reading mechanism is solely satisfied at the superhelical location 2 (SHL2). While SHL2 acts transparently for solvent-facing Sox binding, among the other two positions, SHL4 permits only shape reading. The final position, SHL0 (dyad), on the other hand, allows no reading mechanism. These findings demonstrate that Sox-based nucleosome recognition is essentially guided by intrinsic nucleosome properties, permitting varying degrees of DNA recognition.

Rapid and efficient ambient temperature X-ray crystal structure determination at Turkish Light Source.

High-resolution biomacromolecular structure determination is essential to better understand protein function and dynamics. Serial crystallography is an emerging structural biology technique which has fundamental limitations due to either sample volume requirements or immediate access to the competitive X-ray beamtime. Obtaining a high volume of well-diffracting, sufficient-size crystals while mitigating radiation damage remains a critical bottleneck of serial crystallography. As an alternative, we introduce the plate-reader module adapted for using a 72-well Terasaki plate for biomacromolecule structure determination at a convenience of a home X-ray source. We also present the first ambient temperature lysozyme structure determined at the Turkish light source (Turkish DeLight). The complete dataset was collected in 18.5 min with resolution extending to 2.39 Å and 100% completeness. Combined with our previous cryogenic structure (PDB ID: 7Y6A), the ambient temperature structure provides invaluable information about the structural dynamics of the lysozyme. Turkish DeLight provides robust and rapid ambient temperature biomacromolecular structure determination with limited radiation damage.

PROT-ON: A structure-based detection of designer PROTein interface MutatiONs.

The mutation-induced changes across protein-protein interfaces have often been observed to lead to severe diseases. Therefore, several computational tools have been developed to predict the impact of such mutations. Among these tools, FoldX and EvoEF1 stand out as fast and accurate alternatives. Expanding on the capabilities of these tools, we have developed the PROT-ON (PROTein-protein interface mutatiONs) framework, which aims at delivering the most critical protein interface mutations that can be used to design new protein binders. To realize this aim, PROT-ON takes the 3D coordinates of a protein dimer as an input. Then, it probes all possible interface mutations on the selected protein partner with EvoEF1 or FoldX. The calculated mutational energy landscape is statistically analyzed to find the most enriching and depleting mutations. Afterward, these extreme mutations are filtered out according to stability and optionally according to evolutionary criteria. The final remaining mutation list is presented to the user as the designer mutation set. Together with this set, PROT-ON provides several residue- and energy-based plots, portraying the synthetic energy landscape of the probed mutations. The stand-alone version of PROT-ON is deposited at https://github.com/CSB-KaracaLab/prot-on. The users can also use PROT-ON through our user-friendly web service http://proton.tools.ibg.edu.tr:8001/ (runs with EvoEF1 only). Considering its speed and the range of analysis provided, we believe that PROT-ON presents a promising means to estimate designer mutations.

Differential Occupancy and Regulatory Interactions of KDM6A in Bladder Cell Lines.

Epigenetic deregulation is a critical theme which needs further investigation in bladder cancer research. One of the most highly mutated genes in bladder cancer is KDM6A, which functions as an H3K27 demethylase and is one of the MLL3/4 complexes. To decipher the role of KDM6A in normal versus tumor settings, we identified the genomic landscape of KDM6A in normal, immortalized, and cancerous bladder cells. Our results showed differential KDM6A occupancy in the genes involved in cell differentiation, chromatin organization, and Notch signaling depending on the cell type and the mutation status of KDM6A. Transcription factor motif analysis revealed HES1 to be enriched at KDM6A peaks identified in the T24 bladder cancer cell line; moreover, it has a truncating mutation in KDM6A and lacks a demethylase domain. Our co-immunoprecipitation experiments revealed TLE co-repressors and HES1 as potential truncated and wild-type KDM6A interactors. With the aid of structural modeling, we explored how truncated KDM6A could interact with TLE and HES1, as well as RUNX and HHEX transcription factors. These structures provide a solid means of studying the functions of KDM6A independently of its demethylase activity. Collectively, our work provides important contributions to the understanding of KDM6A malfunction in bladder cancer.

Genetic complementation screening and molecular docking give new insight on phosphorylation-dependent Mastl kinase activation.

Mastl is a mitotic kinase that is essential for error-free chromosome segregation. It is an atypical member of AGC kinase family, possessing a unique non-conserved middle region. The mechanism of Mastl activation has been studied extensively in vitro. Phosphorylation of several residues were identified to be crucial for activation. These sites correspond to T193 and T206 in the activation loop and S861 in the C-terminal tail of mouse Mastl. To date, the significance of these phosphosites was not confirmed in intact mammalian cells. Here, we utilize a genetic complementation approach to determine the essentials of mammalian Mastl kinase activation. We used tamoxifen-inducible conditional knockout mouse embryonic fibroblasts to delete endogenous Mastl and screened various mutants for their ability to complement its loss. S861A mutant was able to complement endogenous Mastl loss. In parallel, we performed computational molecular docking studies to evaluate the significance of this residue for kinase activation. Our in-depth sequence and structure analysis revealed that Mastl pS861 does not belong to a conformational state, where the phosphoresidue contributes to C-tail docking. C-tail of Mastl is relatively short and it lacks a hydrophobic (HF) motif that would otherwise help its anchoring over N-lobe, required for the final steps of kinase activation. Our results show that phosphorylation of Mastl C-tail turn motif (S861) is dispensable for kinase function in cellulo.Communicated by Ramaswamy H. Sarma.

Enzymatic Generation of Double-Modified AdoMet Analogues and Their Application in Cascade Reactions with Different Methyltransferases.

Methyltransferases (MTases) have become an important tool for site-specific alkylation and biomolecular labelling. In biocatalytic cascades with methionine adenosyltransferases (MATs), transfer of functional moieties has been realized starting from methionine analogues and ATP. However, the widespread use of S-adenosyl-l-methionine (AdoMet) and the abundance of MTases accepting sulfonium centre modifications limit selective modification in mixtures. AdoMet analogues with additional modifications at the nucleoside moiety bear potential for acceptance by specific MTases. Here, we explored the generation of double-modified AdoMets by an engineered Methanocaldococcus jannaschii MAT (PC-MjMAT), using 19 ATP analogues in combination with two methionine analogues. This substrate screening was extended to cascade reactions and to MTase competition assays. Our results show that MTase targeting selectivity can be improved by using bulky substituents at the N6 of adenine. The facile access to >10 new AdoMet analogues provides the groundwork for developing MAT-MTase cascades for orthogonal biomolecular labelling.

Novel biallelic variants affecting the OTU domain of the gene OTUD6B associate with severe intellectual disability syndrome and molecular dynamics simulations.

Intellectual developmental disorder with dysmorphic facies, seizures, and distal limb anomalies (IDDFSDA) is an autosomal recessive multisystem disorder caused by compound heterozygous or homozygous variants in the gene OTUD6B. Herein, we describe novel pathogenic compound heterozygous variants in OTUD6B identified via whole-exome sequencing in an index case exhibited the severe IDDFSDA phenotype. The potential pathogenicity of the novel frameshift and missense variants in the index case was investigated using in silico tools. The truncating frameshift variant in one allele was predicted to undergo degradation via nonsense-mediated decay of the mRNA molecule. To predict the severity of the damage to the protein caused by the missense variant in the other allele and its effects on phenotypic severity was further investigated together with a previously reported first homozygous missense variant in the same domain in another patient with a less severe IDDFSDA phenotype using structural modeling and molecular dynamics (MD) simulations for the first time. Based on these analyzes, it is anticipated that Tyr216Cys in the earlier reported case with less severe IDDFSDA will lead to localized destabilization, whereas Ile274Arg in the presented index case with the severe IDDFSDA phenotype will lead to significant distortion in the overall fold of OTUD6B. Our findings suggest that compound LOF and ultrarare missense variants may be contribute to the underlying variability expressivity associated with this disorder. In conclusion, our findings support that the clinical severity could be related with the predicted functional severity of the variations in OTUD6B. However, additional functional studies are required.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.

VARS1 mutations associated with neurodevelopmental disorder are located on a short amino acid stretch of the anticodon-binding domain.

Majority of 37 human aminoacyl tRNA synthetases have been incriminated in diverse, mostly recessive, genetic diseases. In accordance with this, we uncovered a novel homozygous valyl-tRNA synthetase 1 (VARS1) gene variant, leading to p.T1068M mutation. As in the previously reported VARS1 mutations, the affected individual harboring p.T1068M was experiencing a neurodevelopmental disorder with intractable seizures, psychomotor retardation, and microcephaly. To link this phenotypic outcome with the observed genotype, we structurally modeled human VARS1 and interpreted p.T1068M within the spatial distribution of previously reported VARS1 variants. As a result, we uncovered that p.T1068M is clustered with three other pathogenic mutations in a 15 amino acid long stretch of the VARS1 anticodon-binding domain. While forming a helix-turn-helix motif within the anticodon-binding domain, this stretch harbors one-fourth of the reported VARS1 mutations. Here, we propose that these clustered mutations can destabilize the interactions between the anticodon-binding and the tRNA synthetase domains and thus hindering the optimal enzymatic activity of VARS1. We expect that the depiction of this mutation cluster will pave the way for the development of drugs, capable of alleviating the functional impact of these mutations.

A C-term truncated EIF2Bγ protein encoded by an intronically polyadenylated isoform introduces unfavorable EIF2Bγ-EIF2γ interactions.

Eukaryotic translation initiates upon recruitment of the EIF2-GTP·Met-tRNAi ternary complex (TC) to the ribosomes. EIF2 (α, ß, γ subunits) is a GTPase. The GDP to GTP exchange within EIF2 is facilitated by the guanine nucleotide exchange factor EIF2B (α-ε subunits). During stress-induced conditions, phosphorylation of the α-subunit of EIF2 turns EIF2 into an inhibitor of EIF2B. In turn, inhibition of EIF2B decreases TC formation and triggers the internal stress response (ISR), which determines the cell fate. Deregulated ISR has been linked to neurodegenerative disorders and cancer, positioning EIF2B as a promising therapeutic target. Hence, a better understanding of the mechanisms/factors that regulate EIF2B activity is required. Here, combining transcript and protein level analyses, we describe an intronically polyadenylated (IPA) transcript of EIF2B's γ-subunit. We show that the IPA mRNA isoform is translated into a C-terminus truncated protein. Using structural modeling, we predict that the truncated EIF2Bγ protein has unfavorable interactions with EIF2γ, leading to a potential decrease in the stability of the nonproductive EIF2:EIF2B complex. While we discovered and confirmed the IPA mRNA isoform in breast cancer cells, the expression of this isoform is not cancer-specific and is widely present in normal tissues. Overall, our data show that a truncated EIF2Bγ protein co-exists with the canonical protein and is an additional player to regulate the equilibrium between productive and nonproductive states of the EIF2:EIF2B complex. These results may have implications in stress-induced translation control in normal and disease states. Our combinatorial approach demonstrates the need to study noncanonical mRNA and protein isoforms to understand protein interactions and intricate molecular mechanisms.

Assessment of the CASP14 assembly predictions.

In CASP14, 39 research groups submitted more than 2500 3D models on 22 protein complexes. In general, the community performed well in predicting the fold of the assemblies (for 80% of the targets), although it faced significant challenges in reproducing the native contacts. This is especially the case for the complexes without whole-assembly templates. The leading predictor, BAKER-experimental, used a methodology combining classical techniques (template-based modeling, protein docking) with deep learning-based contact predictions and a fold-and-dock approach. The Venclovas team achieved the runner-up position with template-based modeling and docking. By analyzing the target interfaces, we showed that the complexes with depleted charged contacts or dominating hydrophobic interactions were the most challenging ones to predict. We also demonstrated that if AlphaFold2 predictions were at hand, the interface prediction challenge could be alleviated for most of the targets. All in all, it is evident that new approaches are needed for the accurate prediction of assemblies, which undoubtedly will expand on the significant improvements in the tertiary structure prediction field.

Predicting the Specificity- Determining Positions of Receptor Tyrosine Kinase Axl.

Owing to its clinical significance, modulation of functionally relevant amino acids in protein-protein complexes has attracted a great deal of attention. To this end, many approaches have been proposed to predict the partner-selecting amino acid positions in evolutionarily close complexes. These approaches can be grouped into sequence-based machine learning and structure-based energy-driven methods. In this work, we assessed these methods' ability to map the specificity-determining positions of Axl, a receptor tyrosine kinase involved in cancer progression and immune system diseases. For sequence-based predictions, we used SDPpred, Multi-RELIEF, and Sequence Harmony. For structure-based predictions, we utilized HADDOCK refinement and molecular dynamics simulations. As a result, we observed that (i) sequence-based methods overpredict partner-selecting residues of Axl and that (ii) combining Multi-RELIEF with HADDOCK-based predictions provides the key Axl residues, covered by the extensive molecular dynamics simulations. Expanding on these results, we propose that a sequence-structure-based approach is necessary to determine specificity-determining positions of Axl, which can guide the development of therapeutic molecules to combat Axl misregulation.

Autosomal recessive variants in TUBGCP2 alter the γ-tubulin ring complex leading to neurodevelopmental disease.

Microtubules help building the cytoskeleton of neurons and other cells. Several components of the gamma-tubulin (γ-tubulin) complex have been previously reported in human neurodevelopmental diseases. We describe two siblings from a consanguineous Turkish family with dysmorphic features, developmental delay, brain malformation, and epilepsy carrying a homozygous mutation (p.Glu311Lys) in TUBGCP2 encoding the γ-tubulin complex 2 (GCP2) protein. This variant is predicted to disrupt the electrostatic interaction of GCP2 with GCP3. In primary fibroblasts carrying the variant, we observed a faint delocalization of γ-tubulin during the cell cycle but normal GCP2 protein levels. Through mass spectrometry, we observed dysregulation of multiple proteins involved in the assembly and organization of the cytoskeleton and the extracellular matrix, controlling cellular adhesion and of proteins crucial for neuronal homeostasis including axon guidance. In summary, our functional and proteomic studies link TUBGCP2 and the γ-tubulin complex to the development of the central nervous system in humans.