The characterization of protein binding sites is a major challenge in computational biology. Proteins interact with a wide variety of molecules and understanding of such complex interactions is essential to gain deeper knowledge of protein function. Shape complementarity is known to be important in determining protein-ligand interactions. Furthermore, these protein structural features have been shown to be useful in assisting medicinal chemists during lead discovery and optimization.

Background: Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines. Results: pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder’s capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook. Conclusions: We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.

The nucleocapsid (N) protein of the SARS-CoV-2 virus, the causal agent of COVID-19, is a multifunction phosphoprotein that plays critical roles in the virus life cycle, including transcription and packaging of the viral RNA. To play such diverse roles, the N protein has two globular RNA-binding modules, the N- (NTD) and C-terminal (CTD) domains, which are connected by an intrinsically disordered region. Despite the wealth of structural data available for the isolated NTD and CTD, how these domains are arranged in the full-length protein and how the oligomerization of N influences its RNA-binding activity remains largely unclear. Herein, using experimental data from electron microscopy and biochemical/biophysical techniques combined with molecular modeling and molecular dynamics simulations, we show that, in the absence of RNA, the N protein formed structurally dynamic dimers, with the NTD and CTD arranged in extended conformations. However, in the presence of RNA, the N protein assumed a more compact conformation where the NTD and CTD are packed together. We also provided an octameric model for the full-length N bound to RNA that is consistent with electron microscopy images of the N protein in the presence of RNA. Together, our results shed new light on the dynamics and higher-order oligomeric structure of this versatile protein.

Molecular interactions that modulate catalytic processes occur mainly in cavities throughout the molecular surface. Such interactions occur with specific small molecules due to geometric and physicochemical complementarity with the receptor. In this scenario, we present KVFinder-web, an open-source web-based application of parKVFinder software for cavity detection and characterization of biomolecular structures. The KVFinder-web has two independent components: a RESTful web service and a web graphical portal. Our web service, KVFinder-web service, handles client requests, manages accepted jobs, and performs cavity detection and characterization on accepted jobs. Our graphical web portal, KVFinder-web portal, provides a simple and straightforward page for cavity analysis, which customizes detection parameters, submits jobs to the web service component, and displays cavities and characterizations. We provide a publicly available KVFinder-web at https://kvfinder-web.cnpem.br, running in a cloud environment as docker containers. Further, this deployment type allows KVFinder-web components to be configured locally and customized according to user demand. Hence, users may run jobs on a locally configured service or our public KVFinder-web.

Mayaro virus (MAYV) is an emerging arbovirus of the Americas that may cause a debilitating arthritogenic disease. The biology of MAYV is not fully understood and largely inferred from related arthritogenic alphaviruses. Here, we present the structure of MAYV at 4.4 Å resolution, obtained from a preparation of mature, infective virions. MAYV presents typical alphavirus features and organization. Interactions between viral proteins that lead to particle formation are described together with a hydrophobic pocket formed between E1 and E2 spike proteins and conformational epitopes specific of MAYV. We also describe MAYV glycosylation residues in E1 and E2 that may affect MXRA8 host receptor binding, and a molecular “handshake” between MAYV spikes formed by N262 glycosylation in adjacent E2 proteins. The structure of MAYV is suggestive of structural and functional complexity among alphaviruses, which may be targeted for specificity or antiviral activity.

Background: Signaling by the vitamin A-derived morphogen retinoic acid (RA) is required at multiple steps of cardiac development. Since conversion of retinaldehyde to RA by retinaldehyde dehydrogenase type II (ALDH1A2, a.k.a RALDH2) is critical for cardiac development, we screened patients with congenital heart disease (CHDs) for genetic variation at the ALDH1A2 locus.Methods: One-hundred and thirty-three CHD patients were screened for genetic variation at the ALDH1A2 locus through bi-directional sequencing. In addition, six SNPs (rs2704188, rs1441815, rs3784259, rs1530293, rs1899430) at the same locus were studied using a TDT-based association approach in 101 CHD trios. Observed mutations were modeled through molecular mechanics (MM) simulations using the AMBER 9 package, Sander and Pmemd programs. Sequence conservation of observed mutations was evaluated through phylogenetic tree construction from ungapped alignments containing ALDH8 s, ALDH1Ls, ALDH1 s and ALDH2 s. Trees were generated by the Neighbor Joining method. Variations potentially affecting splicing mechanisms were cloned and functional assays were designed to test splicing alterations using the pSPL3 splicing assay.Results: We describe in Tetralogy of Fallot (TOF) the mutations Ala151Ser and Ile157Thr that change non-polar to polar residues at exon 4. Exon 4 encodes part of the highly-conserved tetramerization domain, a structural motif required for ALDH oligomerization. Molecular mechanics simulation studies of the two mutations indicate that they hinder tetramerization. We determined that the SNP rs16939660, previously associated with spina bifida and observed in patients with TOF, does not affect splicing. Moreover, association studies performed with classical models and with the transmission disequilibrium test (TDT) design using single marker genotype, or haplotype information do not show differences between cases and controls.Conclusion: In summary, our screen indicates that ALDH1A2 genetic variation is present in TOF patients, suggesting a possible causal role for this gene in rare cases of human CHD, but does not support the hypothesis that variation at the ALDH1A2 locus is a significant modifier of the risk for CHD in humans.