Biology subjects

Marti, M. A.

Publications and source records attributed to Marti, M. A..

6 recordsLinked to original sources

bioRxiv2025

A structure and function-based complete mutational map of Human Hemoglobin using AI

Hemoglob+in (Hb), a well-characterized protein central to oxygen transport and molecular medicine, serves as a model for studying how sequence variations influence protein structure and function. Its precise activity depends on tightly regulated structural dynamics, which can be disrupted by mutations that give rise to structural hemoglobinopathies--including sickle cell disease, unstable hemoglobins, methemoglobins, and hemoglobins with altered oxygen affinity--each associated with distinct functional and clinical consequences.Among genetic variants, missense mutations are the most widely studied in clinical settings. Accurately predicting their clinical impact remains challenging, requiring integration of evolutionary, biochemical, and structural data. While broad deep learning models like AlphaMissense show promise, they often lack interpretability and protein-specific precision. This motivates the development of focused models that leverage detailed knowledge of individual proteins, like hemoglobin, to improve both predictive power and mechanistic understanding. In this work, we conducted a comprehensive analysis of all known and potential human adult hemoglobin (HbA) variants, guided by the hypothesis that a deep understanding of the sequence-structure-function relationship in Hb can yield interpretable and predictive insights into the functional and clinical consequences of single amino acid substitutions. We curated an updated dataset of HbA variants annotated with their clinical classifications--Benign, Pathogenic, or of Uncertain Significance (VUS)--and systematically mapped each to a range of features, including structural location and classification, predicted impact on folding stability, and evolutionary conservation. Using this data, we developed a pathogenicity prediction model and benchmarked it against AlphaMissense, demonstrating strong and complementary performance. Additionally, we generated a complete mutational landscape of all possible single amino acid substitutions (SAS) in HbA, providing a resource for future clinical interpretation. Our findings provide insight into the molecular basis for variant effects in HbA and highlight the utility of combining structure-informed features with Machine Learning (ML) for variant interpretation. Moreover, our results offer a framework for evaluating the portability and interpretability of variant effect predictors across structurally dynamic systems, with implications in the improvement of variant classification in other protein families.

bioinformatics↗

bioRxiv2025

Integrating AlphaFold2 Models and Clinical Data to Improve the Assessment of Short Linear Motifs (SLiMs) and Their Variants' Pathogenicity

Short Linear Motifs (SLiMs) are protein functionally relevant regions that mediate reversible protein-protein interactions. Variants that disrupt SLiMs can lead to numerous Mendelian diseases. Although various bioinformatic tools have been developed to identify SLiMs, most suffer from low specificity. In our previous work, we demonstrated that integrating sequence variant information with structural analysis can enhance the prediction of true functional SLiMs while simultaneously generating tolerance matrices that indicate whether each of the 19 possible single amino acid substitutions (SASs) is tolerated. However, the scarcity of representative crystallographic structures of SLiM-receptor complexes posed a significant limitation. In this study, we demonstrate that these interactions can be modeled using AlphaFold2 (AF2) to generate high-quality structures that serve as input for our MotSASi method. These AF2-derived structures show robust performance, both in reproducing known structures deposited in the Protein Data Bank (PDB) and in reflecting the deleterious effects of known sequence variants. This updated version of MotSASi expands the repertoire of high-confidence predicted SLiMs and provides a comprehensive catalog of variants located within SLiMs, along with their respective deleteriousness assessments. When compared to AlphaMissense, MotSASi demonstrates superior performance in predicting variant deleteriousness. By contributing to the accurate identification and interpretation of variants, this work aligns with ACMG/AMP standards and aims to improve diagnostic rates in clinical genomics. Author SummaryProteins interact with each other in highly specific ways to carry out vital biological functions. Short Linear Motifs (SLiMs) are small regions within proteins that mediate many of these reversible interactions. Changes in SLiMs can disrupt these interactions and lead to severe genetic disorders. Identifying SLiMs accurately has been a longstanding challenge, as many computational tools suffer from low specificity. Previously, we developed a method, MotSASi, that combines sequence variation data and structural analysis to improve SLiM prediction and assess the impact of single amino acid substitutions (SASs). However, the lack of available structural data limited its application. In this study, we demonstrate that structures generated using AlphaFold2 (AF2) can overcome this limitation. These high-quality AF2 models reliably reproduce known structures and capture the harmful effects of sequence variations. By integrating AF2 models, the updated MotSASi method identifies more high-confidence SLiMs and provides detailed assessments of the variants within them. MotSASi outperforms existing tools, such as AlphaMissense, in predicting the impact of genetic variants, offering insights aligned with clinical standards. This advancement can aid in understanding disease mechanisms and improving genetic diagnostics in clinical genomics.

bioinformatics↗

bioRxiv2024

Genome sequence and characterization of a hypervirulent BI/NAP1/027 Clostridioides difficile (CDC20121308)

Clostridioides difficile is a gram-positive bacterium implicated in antibiotic-associated diarrhea. The use of antibiotics alters the gut microbiota, rendering the host susceptible to infection by C. difficile. This pathogen colonizes the large intestine of humans and animals leading to asymptomatic carriage or clinical manifestations such as toxic megacolon and fulminant colitis depending on a wide range of pathogen and host factors. The emergence of BI/NAP1/027 strains in North America and the spread of these hypervirulent ribotypes worldwide have been linked to the increase in incidence and severity of C. difficile infections (CDI) over the last decade. In this work, we aimed to characterize the BI/NAP1/027 C. difficile commercial strain CDC20121308 widely employed in the study of host-pathogen interactions. The genome sequence was obtained using a whole-genome shotgun strategy. A total of 3,717 coding sequences (CDS) and 45 tRNAs were predicted. The annotation of the CDC20121308 strain identified 26% of CDS into RAST subsystems. We also detected the presence of RT 027 lineage markers such as thyA, cdtA, cdtB and tcdC 18bp-deletion. Moreover, the genome of CDC20121308 had 11 genes devoted to resistance to toxic compounds, antibiotics (e.g. Tetracycline (Tet) and Vancomycin (Van)) and disinfecting agents as predicted using CARD. C. difficile CDC20121308 resistance to Van and Tet was confirmed by broth microdilution assay. Crystal violet staining demonstrated biofilm formation, which could be associated with antibiotic resistance and pathogenicity. Additionally, we observed a spreading diffuse growth in soft agar tubes, suggesting a motile phenotype. Lastly, a genomic region containing Type 4 Secretory System components such as virD4, virB4, and virB6 was identified. In conclusion, our results allowed a genomic and functional characterization of the BI/NAP1/027 C. difficile CDC20121308 strain. We demonstrated the presence of several genes associated with pathogenesis that were validated by experimental assays. This study provides additional data for the use of this highly virulence commercial strain of epidemiological relevance in research works involving in vitro and in vivo approaches.

bioinformatics↗

bioRxiv2023

Wide Transition-State Ensemble as Key Component for Enzyme Catalysis

Transition-state theory has provided the theoretical framework to explain the enormous rate accelerations of chemical reactions by enzymes. Given that proteins display large ensembles of conformations, unique transition states would pose a huge entropic bottleneck for enzyme catalysis. To shed light on this question, we studied the nature of the enzymatic transition state for the phosphoryl-transfer step in adenylate kinase by quantum-mechanics/molecular-mechanics calculations. We find a structurally wide set of energetically equivalent configurations that lie along the reaction coordinate and hence a broad transition-state ensemble (TSE). A conformationally delocalized ensemble, including asymmetric transition states, is rooted in the macroscopic nature of the enzyme. The computational results are buttressed by enzyme kinetics experiments that confirm the decrease of the entropy of activation predicted from such wide TSE. Transition-state ensembles as a key for efficient enzyme catalysis further boosts a unifying concept for protein folding and conformational transitions underlying protein function.

biophysics↗

bioRxiv2023

The Evolution of Local Energetic Frustration in Protein Families

Energetic local frustration offers a biophysical perspective to interpret the effects of sequence variability on protein families. Here we present a methodology to analyze local frustration patterns within protein families that allows us to uncover constraints related to stability and function, and identify differential frustration patterns in families with a common ancestry. We have analyzed these signals in very well studied cases such as PDZ, SH3, and {beta} globins and RAS families. Recent advances in protein structure prediction make it possible to analyze a vast majority of the protein space. An automatic and unsupervised proteome-wide analysis on the SARS-CoV-2 virus demonstrates the potential of our approach to enhance our understanding of the natural phenotypic diversity of protein families beyond single protein instances. We have applied our method to modify biophysical properties of natural proteins based on their family properties, as well as perform unsupervised analysis of large datasets to shed light on the physicochemical signatures of poorly characterized proteins such as emergent pathogens.

bioinformatics↗

bioRxiv2021

MotSASi: Functional Short Linear Motifs (SLiMs) prediction based on genomic single nucleotide variants and structural data

Short linear motifs (SLiMs) are key to cell physiology mediating reversible protein-protein interactions. Precise identification of SLiMs remains a challenge, being the main drawback of most bioinformatic prediction tools their low specificity (high number of false positives). An important, usually overlooked, aspect is the relation between SLiMs mutations and disease. The presence of variants in each residue position can be used to assess the relevance of the corresponding residue(s) for protein function, and its (in)tolerance to change. In the present work, we combined sequence variant information and structural analysis of the energetic impact of single amino acid substitution (SAS) in SLiM-Receptor complex structure, and showed that it significantly improves prediction of true functional SLiMs. Our strategy is based on building a SAS tolerance matrix that shows, for each position, whether one of the possible 19 SAS is tolerated or not. Herein we present the MotSASi strategy and analyze in detail 4 SLiMs involved in intracellular protein trafficking. Our results show that inclusion of variant and sequence information significantly improves both prediction of true SLiMs and rejection of false positives, while also allowing better classification of variants inside SLiMs, a results with a direct impact in clinical genomics.

molecular biology↗