Human thymidylate synthase is the rate-limiting enzyme in the de novo synthesis of 2'-deoxythymidine-5'-monophosphate. dUMP (pyrimidine) and folate binding site hTS inhibitors showed resistance in colorectal cancer (CRC). In the present study, we have performed virtual screening of the pyrido[2,3-d]pyrimidine database, followed by binding free energy calculations, and pharmacophore mapping to design novel pyrido[2,3-d]pyrimidine derivatives to stabilize inactive confirmation of hTS. A library of 42 molecules was designed. Based on the molecular docking studies, four ligands (T36, T39, T40, and T13) were identified to have better interactions and docking scores with the catalytic sites [dUMP (pyrimidine) and folate binding sites] of hTS protein than standard drug, raltitrexed. To validate efficacy of the designed molecules, we performed molecular dynamics simulation studies at 1000 ns with principal component analysis and binding free energy calculations on the hTS protein, also drug likeness properties of all hits were in acceptable range. Compounds T36, T39, T40, and T13 interacted with the catalytic amino acid (Cys195), an essential amino acid for anticancer activity. The designed molecules stabilized the inactive conformation of hTS, resulting in the inhibition of hTS. The designed compounds will undergo synthesis and biological evaluation, which may yield selective, less toxic, and highly potent hTS inhibitors.Communicated by Ramaswamy H. Sarma.

Virtual screening, structure based pharmacophore mapping, and molecular simulation studies of pyrido[2,3-d]pyrimidines as selective thymidylate synthase inhibitors.

Missense mutations are among the most frequently occurring variants in exon regions and may lead to pathogenic phenotypes. Computational methods predicting effects of missense mutations play important roles in assessing pathogenicity of these variants. While a number of methods have been developed based on analysis of sequence conservation, co-evolution, and protein structures, effectively determining mutation effects on biochemical function remains challenging. Here we report the method of Structure-Based Pathogenicity Relationship Identifier ( SPRI ) that can accurately evaluate pathological effects of missense mutations and identify those that are deleterious. In addition to sequence analysis, our method quantifies short-, intermediate-, and long-range topological interactions at atomic and residue-level through geometric computation. Our method also explicitly computes surface pockets, and considers mutagenic changes in biophysical properties. The SPRI method performs favorably in identifying deleterious mutations and in quantifying their pathogenicity compared to current state-of-the-art methods in Mendelian diseases, as measured by several performance metrics. In addition, SPRI captures com-mon properties among pathological missense mutations of both germline and somatic origins. Furthermore, the pathogenicity model is transferable across Mendelian diseases and cancer types as SPRI makes accurate predictions on effects of cancer driver mutations. Overall, our method allows detection of deleterious mutations contributing to single gene disorders such as Mendelian-type diseases, as well as complex-trait diseases such as cancer. Our results demonstrate that accurate assessment of pathological effects of missense mutation can be obtained when structural information and biophysical constraints are properly extracted and integrated with evolutionary information. We expect that our method can be broadly applied to assess missense mutations regardless of the mutational origins (germline or somatic), or disease types. identifying cancer driver mutations. Our method can correctly classify 110 mutations out of the 122 structure-mapped mutations as deleterious. We also tested the methods of PROVEAN, Polyphen-2 and PMUT , which have comparable performance, identifying 109, 105, and 103 deleterious mutations, respectively. EVmutation identifies 94 deleterious mutations, with 9 incorrect predictions and 19 missing predictions. Rhapsody identifies 78 out of the 122 deleterious mutations, at a significantly reduced level of effectiveness than Mendelian disorders. FATHMM identifies 65 deleterious mutations, with 54 incorrect predictions and 3 missing predictions.

Structure-Based Pathogenicity Relationship Identifier (SPRI): A Novel Structure-Guided Method to Evaluate Pathological Effect of Missense Mutations

In silico methods evaluating the mutation effects of missense mutations are providing an important approach for understanding mutations in personal genomes and identifying disease-relevant biomarkers. However, existing methods, including deep learning methods, heavily rely on sequence-aware information, and do not fully leverage the potential of available 3D structural information. In addition, these methods may exhibit an inability to predict mutations in domains difficult to formulate sequence-based embeddings. In this study, we introduce a novel rationale-guided graph neural network AlphaGMut to evaluate mutation effects and to distinguish pathogenic mutations from neutral mutations. We compute the alpha shapes of protein structures to obtain atomic-resolution edge connectivities and map them to an accurate residue-level graph representation. We then compute structural-, topological-, biophysical-, and sequence properties of the mutation sites, which are assigned as node attributes in the graph. These node attributes could effectively guide the graph neural network to learn the difference between pathogenic and neutral mutations using k-hop message passing with a short training period. We demonstrate that AlphaGMut outperforms state-of-the-art methods, including DeepMind's AlphaMissense, in many performance metrics. In addition, AlphaGMut has the advantage of performing well in alignment-free settings, which provides broader prediction coverage and better generalization compared to current methods requiring deep sequence-aware information. 

pdf/alphagmut-a-rationale-guided-alpha-shape-graph-neural-35cg168rtl.pdf

ALPHAGMUT: A Rationale-Guided Alpha Shape Graph Neural Network to
  Evaluate Mutation Effects

Motivation: 3D structures of proteins provide rich information for understanding their biochemical roles. Identifying the representative protein structures for protein sequences is essential for analysis of proteins at proteome scale. However, there are technical difficulties in identifying the representative structure of a given protein sequence and providing accurate mapping of residue indices. Existing databases of mapping between structures and sequences are usually static that are not suitable for studying proteomes with frequent gene model revisions. They often do not provide reliable and consistent representative structures that maximizes sequence coverage. Furthermore, proteins isomers are usually not properly resolved. Results: To overcome these difficulties, we have developed a computational pipeline called SeqMapPDB to provide high-quality representative PDB structures of given sequences. It provides mapping to structures that fully cover the sequences when available, or to the set of partial non-overlapping structural domains that maximally cover the query sequence. The residue indices are accurate mapped and isomeric proteins are resolved. SeqMapPDB is efficient and can rapidly carry out proteome-wide mapping to the selected version of reference genomes in real-time. Furthermore, SeqMapPDB provides the flexibility of a stand-alone pipeline for large scale mapping of in-house sequence and structure data. Availability: Our method is available at https://bitbucket.org/lianglabuic/seqmappdb with GNU GPL license.

SeqMapPDB: A Standalone Pipeline to Identify Representative Structures of Protein Sequences and Mapping Residue Indices in Real-Time at Proteome Scale

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SeqMapPDB: A Standalone Pipeline to Identify Representative Structures of Protein Sequences and Mapping Residue Indices in Real-Time at Proteome Scale