Protein Structure Prediction Research Articles

Natural occurrence, infection dynamics, and molecular characterization of nucleopolyhedrovirus (SpfrNPV) infecting fall armyworm, Spodoptera frugiperda (J. E. Smith) from maize ecosystems in Gujarat, India

This comprehensive study investigates the stage-wise infection dynamics of Nucleopolyhedrovirus (NPV) in fall armyworm, Spodoptera frugiperda (J. E. Smith) larvae across distinct stages of maize crop in Panchmahal and Mahisagar districts of Gujarat, India during the kharif season in 2021. The examination of month-wise mean data reveals a pronounced pattern of NPV prevalence, with the highest infection observed during the cob formation stage (28.74 %), followed by flowering and tasseling (20.23 %), and the vegetative stage (13.93 %). Block specific analyses highlight variations in infection rates, with notable differences between districts and stages. Correlation coefficient analysis indicates a significant and positive relationship between specific crop stages and NPV infection. Regression equations are established, demonstrating a positive increase in natural NPV infection during different crop stages. Weather parameter analysis reveals significant correlations between temperature, humidity, sunshine hours, and NPV infection. The phylogenetic analysis of NPV polyhedrin sequences indicates species-specific clustering within S. frugiperda and a broader genus-specific association. Motif pattern analysis identifies conserved motifs in NPV polyhedrins. Protein structure predictions and Molecular docking studies predicted interactions between polyhedrin and chitinase, suggesting a potential role in the insect's defense against NPV. The bioassay results indicated increased susceptibility of early larvae to NPV, emphasizing the potential for optimizing biocontrol strategies. Overall, this study contributes valuable insights into the ecological and molecular aspects of NPV infection dynamics, offering a significance understanding for developing effective pest management strategies and also underscores its significance in promoting sustainable agriculture.

Advances in the application of AlphaFold2: a protein structure prediction model

Protein structure prediction is an important research field in life sciences and medicine, and it is also a key application scenario of artificial intelligence in scientific research. AlphaFold2 is a protein structure prediction system developed by DeepMind based on deep learning, capable of efficiently generating the atomic-scale spatial structure of a protein from the amino acid sequence. It has demonstrated superior performance in the prediction of protein structures since its inception, thus attracting much attention and research. This paper introduces the model architecture, highlights, limitations, and application progress of AlphaFold2. Furthermore, it briefs the capabilities, highlights, and limitations of several other types of protein structure prediction models and prospects the future development direction in this field.

Accurate prediction of antibody function and structure using bio-inspired antibody language model.

In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% nonredundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials. The BALMFold structure prediction server is freely available at https://beamlab-sh.com/models/BALMFold.

Accurate Protein pKa Prediction with Physical Organic Chemistry Guided 3D Protein Representation.

Protein pKa is a fundamental physicochemical parameter that dictates protein structure and function. However, accurately determining protein site-pKa values remains a substantial challenge, both experimentally and theoretically. In this study, we introduce a physical organic approach, leveraging a protein structural and physical-organic-parameter-based representation (P-SPOC), to develop a rapid and intuitive model for protein pKa prediction. Our P-SPOC model achieves state-of-the-art predictive accuracy, with a mean absolute error (MAE) of 0.33 pKa units. Furthermore, we have incorporated advanced protein structure prediction models, like AlphaFold2, to approximate structures for proteins lacking three-dimensional representations, which enhances the applicability of our model in the context of structure-undetermined protein research. To promote broader accessibility within the research community, an online prediction interface was also established at isyn.luoszgroup.com.

CASTpFold: Computed Atlas of Surface Topography of the universe of protein Folds.

Geometric and topological properties of protein structures, including surface pockets, interior cavitiesand cross channels, are of fundamental importance for proteins to carry out their functions. Computed Atlas of Surface Topography of proteins(CASTp) is a widely used web server for locating, delineating, and measuring these geometric and topological properties of protein structures. Recent developments in AI-based protein structure prediction such as AlphaFold2 (AF2) have significantly expanded our knowledge on protein structures. Here we present CASTpFold, a continuation of CASTp that provides accurate and comprehensive identifications and quantifications of protein topography. It now provides (i) results on an expanded database of proteins, including the Protein Data Bank(PDB) and non-singleton representative structures of AlphaFold2 structures, covering 183 million AF2 structures; (ii) functional pockets prediction with corresponding Gene Ontology (GO) terms or Enzyme Commission (EC) numbers for AF2-predicted structuresand (iii) pocket similarity search function for surface and protein-protein interface pockets. The CASTpFold web server is freely accessible at https://cfold.bme.uic.edu/castpfold/.

#662 Characterization of novel gene mutation sites associated with autosomal dominant polycystic kidney disease on 19 chromosomes

Abstract Background and Aims Autosomal dominant polycystic kidney disease (ADPKD) is the most common single-gene inherited kidney disorder, affecting approximately 1 in 500-1000 newborns and often leading to end-stage renal disease. It involves cyst formation in various organs, with renal manifestations including cysts, hematuria, kidney stones, proteinuria, and hypertension. Liver, pancreas, seminal vesicles, spleen, and intracranial aneurysms may also be affected. Genetic diagnosis, particularly for patients without a family history or with atypical imaging, is considered the gold standard. ADPKD is associated with several pathogenic genes, such as PKD1 and PKD2, as well as others like HNF-1B, GANAB, DNAJB11, SEC63, ALG8, ALG9, PRKCSH, LRP5, SEC61B, and SEC63. Understanding the genetic basis is crucial for early diagnosis and identifying high-risk individuals. This study, based on genetic data from patients with renal polycystic kidney disease at Zhong Da Hospital Southeast University, identified novel ADPKD gene mutations. It explored structural changes in proteins, predicting effects on phenotype, to contribute to the enrichment of the ADPKD gene database. Method In a single-center cross-sectional retrospective study, patients with renal multiple cysts diagnosed from January 2019 to February 2023 at the Zhong Da Hospital Southeast University were included. Genetic and clinical data of patients and their families were collected. Unreported novel gene mutation sites associated with ADPKD were identified, and AlphaFold v2.3.1 was employed to predict protein structures. Changes in protein structure before and after mutations were compared to explore genotype-phenotype correlations and enrich the ADPKD gene database. Results The study included 96 patients with renal multiple cysts and their relatives from 62 families. Twelve mutated genes associated with renal cysts were detected in 52 families. Nineteen novel gene mutation sites associated with ADPKD were identified (Table 1, Fig. 1) , including 17 mutations in the PKD1 gene (1 splicing mutation, 7 frameshift mutations, 4 nonsense mutations, 1 whole-codon insertion, and 4 missense mutations), 1 ALG9 missense mutation, and 1 chromosomal structural variation. Truncating mutations in the PKD1 gene were correlated with a more severe clinical phenotype, while non-truncating mutations were associated with greater clinical heterogeneity. Conclusion Numerous novel gene mutation sites associated with ADPKD remain unreported. It is essential to analyze the pathogenicity of these novel mutation sites, establish genotype-phenotype correlations, and enrich the ADPKD gene database.

Advancing AI protein structure prediction and design: From amino acid “bones” to new era of all-atom “flesh”

Advancing AI protein structure prediction and design: From amino acid “bones” to new era of all-atom “flesh”

The c.503A>G polymorphism in ZIP1-II of Pacific oyster Crassostrea gigas associated with zinc content

The Pacific oyster Crassostrea gigas is renowned for its high zinc content, but the significant variation among individuals diminishes its value as a reliable source of zinc supplementation. The Zrt/Irt-like protein 1 (ZIP1), a pivotal zinc transporter that facilitates zinc uptake in various organisms, plays crucial roles in regulating zinc content. In the present study, polymorphisms of a ZIP1 gene in C. gigas (CgZIP1-II) were investigated, and their association with zinc content was evaluated through preliminary association analysis in 41 oysters and verification analysis in another 200 oysters. A total of 17 single nucleotide polymorphisms (SNPs) were identified in the exonic region of CgZIP1-II gene, with c.503A>G significantly associated with zinc content. Protein sequence and structure prediction showed that c.503A>G caused a p.Met110Val nonsynonymous mutation located in the metal-binding region of CgZIP1-II, which could influence its affinity for zinc ions, thereby modulating its zinc transport functionality. These results indicate the potential influence of CgZIP1-II polymorphisms on zinc content and provide candidate markers for selecting C. gigas with high zinc content.

The Functional Significance of High Cysteine Content in Eye Lens γ-Crystallins.

Cataract disease is strongly associated with progressively accumulating oxidative damage to the extremely long-lived crystallin proteins of the lens. Cysteine oxidation affects crystallin folding, interactions, and light-scattering aggregation especially strongly due to the formation of disulfide bridges. Minimizing crystallin aggregation is crucial for lifelong lens transparency, so one might expect the ubiquitous lens crystallin superfamilies (α and βγ) to contain little cysteine. Yet, the Cys content of γ-crystallins is well above the average for human proteins. We review literature relevant to this longstanding puzzle and take advantage of expanding genomic databases and improved machine learning tools for protein structure prediction to investigate it further. We observe remarkably low Cys conservation in the βγ-crystallin superfamily; however, in γ-crystallin, the spatial positioning of Cys residues is clearly fine-tuned by evolution. We propose that the requirements of long-term lens transparency and high lens optical power impose competing evolutionary pressures on lens βγ-crystallins, leading to distinct adaptations: high Cys content in γ-crystallins but low in βB-crystallins. Aquatic species need more powerful lenses than terrestrial ones, which explains the high methionine content of many fish γ- (and even β-) crystallins. Finally, we discuss synergies between sulfur-containing and aromatic residues in crystallins and suggest future experimental directions.

Insights into the Mechanism of Catalytic Activity of Plasmodium Parasite Malate-Quinone Oxidoreductase.

Plasmodium malate-quinone oxidoreductase (MQO) is a membrane flavoprotein catalyzing the oxidation of malate to oxaloacetate and the reduction of quinone to quinol. Recently, using a yeast expression system, we demonstrated that MQO, expressed in place of mitochondrial malate dehydrogenase (MDH), contributes to the TCA cycle and the electron transport chain in mitochondria, making MQO attractive as a promising drug target in Plasmodium malaria parasites, which lack mitochondrial MDH. However, there is little information on the structure of MQO and its catalytic mechanism, information that will be required to develop novel drugs. Here, we investigated the catalytic site of P. falciparum MQO (PfMQO) using our yeast expression system. We generated a model structure for PfMQO with the AI tool AlphaFold and used protein footprinting by acetylation with acetic anhydride to analyze the surface topology of the model, confirming the computational prediction to be reasonably accurate. Moreover, a putative catalytic site, which includes a possible flavin-binding site, was identified by this combination of protein footprinting and structural prediction model. This active site was analyzed by site-directed mutagenesis. By measuring enzyme activity and protein expression levels in the PfMQO mutants, we showed that several residues at the active site are essential for enzyme function. In addition, a single substitution mutation near the catalytic site resulted in enhanced sensitivity to ferulenol, an inhibitor of PfMQO that competes with malate for binding to the enzyme. This strongly supports the notion that the substrate binds to the proposed catalytic site. Then, the location of the catalytic site was demonstrated by structural comparison with a homologous enzyme. Finally, we used our results to propose a mechanism for the catalytic activity of MQO by reference to the mechanism of action of structurally or functionally homologous enzymes.

OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization.

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.

FBXO11 variants are associated with intellectual disability and variable clinical manifestation in Chinese affected individuals.

F-box protein 11 (FBXO11) is a member of F-Box protein family, which has recently been proved to be associated with intellectual developmental disorder with dysmorphic facies and behavioral abnormalities (IDDFBA, OMIM: 618089). In this study, 12 intellectual disability individuals from 5 Chinese ID families were collected, and whole exome sequencing (WES), sanger sequencing, and RNA sequencing (RNA-seq) were conducted. Almost all the affected individuals presented with mild to severe intellectual disability (12/12), global developmental delay (10/12), speech and language development delay (8/12) associated with a range of alternate features including increased body weight (7/12), short stature (6/12), seizures (3/12), reduced visual acuity (4/12), hypotonia (1/12), and auditory hallucinations and hallucinations (1/12). Distinguishingly, malformation was not observed in all the affected individuals. WES analysis showed 5 novel FBXO11 variants, which include an inframe deletion variant, a missense variant, two frameshift variants, and a partial deletion of FBXO11 (exon 22-23). RNA-seq indicated that exon 22-23 deletion of FBXO11 results in a new mRNA structure. Conservation and protein structure prediction demonstrated deleterious effect of these variants. The DEGs analysis revealed 148 differentially expressed genes shared among 6 affected individuals, which were mainly associated with genes of muscle and immune system. Our research is the first report of FBXO11-associated IDDFBA in Chinese individuals, which expands the genetic and clinical spectrum of this newly identified NDD/ID syndrome.

Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning.

Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and AlphaFold 2 methods to explore the molecular level of PDR. We analyzed scRNA-seq data from PDR patients and healthy controls to identify distinct cellular subtypes and gene expression patterns. NMF was used to define specific transcriptional programs in PDR. The oxidative stress-related genes (ORGs) identified within Meta-Program 1 were utilized to construct a predictive model using twelve machine learning algorithms. Furthermore, we employed AlphaFold 2 for the prediction of protein structures, complementing this with molecular docking to validate the structural foundation of potential therapeutic targets. We also analyzed protein-protein interaction (PPI) networks and the interplay among key ORGs. Our scRNA-seq analysis revealed five major cell types and 14 subcell types in PDR patients, with significant differences in gene expression compared to those in controls. We identified three key meta-programs underscoring the role of microglia in the pathogenesis of PDR. Three critical ORGs (ALKBH1, PSIP1, and ATP13A2) were identified, with the best-performing predictive model demonstrating high accuracy (AUC of 0.989 in the training cohort and 0.833 in the validation cohort). Moreover, AlphaFold 2 predictions combined with molecular docking revealed that resveratrol has a strong affinity for ALKBH1, indicating its potential as a targeted therapeutic agent. PPI network analysis, revealed a complex network of interactions among the hub ORGs and other genes, suggesting a collective role in PDR pathogenesis. This study provides insights into the cellular and molecular aspects of PDR, identifying potential biomarkers and therapeutic targets using advanced technological approaches.

Darobactin Substrate Engineering and Computation Show Radical Stability Governs Ether versus C-C Bond Formation.

The Gram-negative selective antibiotic darobactin A has attracted interest owing to its intriguing fused bicyclic structure and unique targeting of the outer membrane protein BamA. Darobactin, a ribosomally synthesized and post-translationally modified peptide (RiPP), is produced by a radical S-adenosyl methionine (rSAM)-dependent enzyme (DarE) and contains one ether and one C-C cross-link. Herein, we analyze the substrate tolerance of DarE and describe an underlying catalytic principle of the enzyme. These efforts produced 51 enzymatically modified darobactin variants, revealing that DarE can install the ether and C-C cross-links independently and in different locations on the substrate. Notable variants with fused bicyclic structures were characterized, including darobactin W3Y, with a non-Trp residue at the twice-modified central position, and darobactin K5F, which displays a fused diether ring pattern. While lacking antibiotic activity, quantum mechanical modeling of darobactins W3Y and K5F aided in the elucidation of the requisite features for high-affinity BamA engagement. We also provide experimental evidence for β-oxo modification, which adds support for a proposed DarE mechanism. Based on these results, ether and C-C cross-link formation was investigated computationally, and it was determined that more stable and longer-lived aromatic Cβ radicals correlated with ether formation. Further, molecular docking and transition state structures based on high-level quantum mechanical calculations support the different indole connectivity observed for ether (Trp-C7) and C-C (Trp-C6) cross-links. Finally, mutational analysis and protein structural predictions identified substrate residues that govern engagement to DarE. Our work informs on darobactin scaffold engineering and further unveils the underlying principles of rSAM catalysis.

Bilinear optimization of protein structure prediction: An exact approach via AB off-lattice model

Protein structure prediction (PSP) remains a central challenge in computational biology due to its inherent complexity and high dimensionality. While numerous heuristic approaches have appeared in the literature, their success varies. The AB off-lattice model, which characterizes proteins as sequences of A (hydrophobic) and B (hydrophilic) beads, presents a simplified perspective on PSP. This work presents a mathematical optimization-based methodology capitalizing on the off-lattice AB model. Dissecting the inherent non-linearities of the energy landscape of protein folding allowed for formulating the PSP as a bilinear optimization problem. This formulation was achieved by introducing auxiliary variables and constraints that encapsulate the nuanced relationship between the protein’s conformational space and its energy landscape. The proposed bilinear model exhibited notable accuracy in pinpointing the global minimum energy conformations on a benchmark dataset presented by the Protein Data Bank (PDB). Compared to traditional heuristic-based methods, this bilinear approach yielded exact solutions, reducing the likelihood of local minima entrapment. This research highlights the potential of reframing the traditionally non-linear protein structure prediction problem into a bilinear optimization problem through the off-lattice AB model. Such a transformation offers a route toward methodologies that can determine the global solution, challenging current PSP paradigms. Exploration into hybrid models, merging bilinear optimization and heuristic components, might present an avenue for balancing accuracy with computational efficiency.

Genome-Informed Trophic Classification and Functional Characterization of Virulence Proteins from the Maize Tar Spot Pathogen Phyllachora maydis.

Phyllachora maydis is an ascomycete foliar fungal pathogen and the causal agent of tar spot in maize. Although P. maydis is considered an economically important foliar pathogen of maize, our general knowledge of the trophic lifestyle and functional role of effector proteins from this fungal pathogen remains limited. Here, we utilized a genome-informed approach to predict the trophic lifestyle of P. maydis and functionally characterized a subset of candidate effectors from this fungal pathogen. Leveraging the most recent P. maydis genome annotation and the CATAStrophy pipeline, we show that this fungal pathogen encodes a predicted carbohydrate-active enzymes (CAZymes) repertoire consistent with that of biotrophs. To investigate fungal pathogenicity, we selected 18 candidate effector proteins that were previously shown to be expressed during primary disease development. We assessed whether these putative effectors share predicted structural similarity with other characterized fungal effectors and determined whether any suppress plant immune responses. Using AlphaFold2 and Foldseek, we showed that one candidate effector, PM02_g1115, adopts a predicted protein structure similar to that of an effector from Verticillium dahlia. Furthermore, transient expression of candidate effector-fluorescent protein fusions in Nicotiana benthamiana revealed two putative effectors, PM02_g378 and PM02_g2610, accumulated predominantly in the cytosol, and three candidate effectors, PM02_g1115, PM02_g7882, and PM02_g8240, consistently attenuated chitin-mediated reactive oxygen species production. Collectively, the results presented herein provide insights into the predicted trophic lifestyle and putative functions of effectors from P. maydis and will likely stimulate continued research to elucidate the molecular mechanisms used by P. maydis to induce tar spot.

A Perspective on Protein Structure Prediction Using Quantum Computers.

Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

AI told you so: navigating protein structure prediction in the era of machine learning

Every protein in our cells has evolved to fold into a specific structure to perform its functions. However, determining these structures experimentally is often challenging, in some cases taking years. Recently, machine-learning algorithms have been designed to predict a protein’s structure directly from its amino acid sequence in minutes to hours. Since the release of the first of these algorithms, AlphaFold and RoseTTAFold, several more have been developed. These have been complemented by tools that leverage the outputs to give structural context to biochemical data, screen for novel protein–protein interactions or even help solve experimental structures. In addition, several public resources have incorporated the predictions into their databases, making the data open to all. Here, we provide a user-focused perspective on machine-learning protein structure prediction, covering some of the popular applications and highlighting caveats. Used effectively, predictive programs offer the potential to speed up research and guide experimental design.

Identification of odorant binding protein and chemosensory protein genes in Protegira songi (Lepidoptera Noctuidae) via transcriptome analysis

The larvae of Protegira songi Chen & Zhang are notorious defoliator specifically infesting the medicinal plant Eucommia ulmoides Oliver. In insects, Odorant binding proteins (OBPs) and chemosensory proteins (CSPs) are highly expressed in olfactory organs and play essential roles in perceiving female pheromone and host volatiles by binding and transporting odorant compounds. In this study, 17 OBPs and 16 CSPs were identified from P. songi by transcriptome analysis. Sequence alignment indicated that PsonOBPs and PsonCSPs share no identity with each other in protein sequence. The overall structures of PsonOBPs and PsonCSPs are mainly constructed by α-helix bundles and loops. But the distribution of conserved cysteine residues and arrangement of α-helix are varied between PsonOBPs and PsonCSPs. Putative ligand binding cavities were found in these predicted protein structures. Expression profiles of PsonOBPs and PsonCSPs in adults and larvae were determined by RT-PCR which showed that 16 PsonOBPs and 11 PsonCSPs were expressed in adult antennae or/and larval head. PsonGOBPs, PsonPBPs, PsonOBP5, PsonOBP7, and PsonCSP6 were preferentially expressed in olfactory associated tissues. Our results suggest that the PsonOBPs and PsonCSPs that contain ligand cavity and are highly enriched in olfactory tissues may play roles in chemosensory perception of P. songi, and the transcriptome data may facilitate the studies on the olfactory mechanism of P. songi specifically choosing E. ulmoides as host.

Exploring the fragmentation efficiency of proteins analyzed by MALDI-TOF-TOF tandem mass spectrometry using computational and statistical analyses.

Matrix-assisted laser desorption/ionization time-of-flight-time-of-flight (MALDI-TOF-TOF) tandem mass spectrometry (MS/MS) is a rapid technique for identifying intact proteins from unfractionated mixtures by top-down proteomic analysis. MS/MS allows isolation of specific intact protein ions prior to fragmentation, allowing fragment ion attribution to a specific precursor ion. However, the fragmentation efficiency of mature, intact protein ions by MS/MS post-source decay (PSD) varies widely, and the biochemical and structural factors of the protein that contribute to it are poorly understood. With the advent of protein structure prediction algorithms such as Alphafold2, we have wider access to protein structures for which no crystal structure exists. In this work, we use a statistical approach to explore the properties of bacterial proteins that can affect their gas phase dissociation via PSD. We extract various protein properties from Alphafold2 predictions and analyze their effect on fragmentation efficiency. Our results show that the fragmentation efficiency from cleavage of the polypeptide backbone on the C-terminal side of glutamic acid (E) and asparagine (N) residues were nearly equal. In addition, we found that the rearrangement and cleavage on the C-terminal side of aspartic acid (D) residues that result from the aspartic acid effect (AAE) were higher than for E- and N-residues. From residue interaction network analysis, we identified several local centrality measures and discussed their implications regarding the AAE. We also confirmed the selective cleavage of the backbone at D-proline bonds in proteins and further extend it to N-proline bonds. Finally, we note an enhancement of the AAE mechanism when the residue on the C-terminal side of D-, E- and N-residues is glycine. To the best of our knowledge, this is the first report of this phenomenon. Our study demonstrates the value of using statistical analyses of protein sequences and their predicted structures to better understand the fragmentation of the intact protein ions in the gas phase.