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Agüero-Chapin G, Galpert-Cañizares D, Domínguez-Pérez D, Marrero-Ponce Y, Pérez-Machado G, Teijeira M, Antunes A. Emerging Computational Approaches for Antimicrobial Peptide Discovery. Antibiotics (Basel) 2022; 11:antibiotics11070936. [PMID: 35884190 PMCID: PMC9311958 DOI: 10.3390/antibiotics11070936] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/01/2022] [Accepted: 07/08/2022] [Indexed: 02/05/2023] Open
Abstract
In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.
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Affiliation(s)
- Guillermin Agüero-Chapin
- CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal;
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
- Correspondence: (G.A.-C.); (A.A.); Tel.: +351-22-340-1813 (G.A.-C. & A.A.)
| | - Deborah Galpert-Cañizares
- Departamento de Ciencia de la Computación, Universidad Central Marta Abreu de Las Villas (UCLV), Santa Clara 54830, Cuba;
| | - Dany Domínguez-Pérez
- CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal;
- Proquinorte, Unipessoal, Lda, Avenida 5 de Outubro, 124, 7º Piso, Avenidas Novas, 1050-061 Lisboa, Portugal
| | - Yovani Marrero-Ponce
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Translacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas and Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Ecuador;
| | - Gisselle Pérez-Machado
- EpiDisease S.L—Spin-Off of Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 46980 Valencia, Spain;
| | - Marta Teijeira
- Departamento de Química Orgánica, Facultade de Química, Universidade de Vigo, 36310 Vigo, Spain;
- Instituto de Investigación Sanitaria Galicia Sur, Hospital Álvaro Cunqueiro, 36213 Vigo, Spain
| | - Agostinho Antunes
- CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal;
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
- Correspondence: (G.A.-C.); (A.A.); Tel.: +351-22-340-1813 (G.A.-C. & A.A.)
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Agüero-Chapin G, Galpert D, Molina-Ruiz R, Ancede-Gallardo E, Pérez-Machado G, De la Riva GA, Antunes A. Graph Theory-Based Sequence Descriptors as Remote Homology Predictors. Biomolecules 2019; 10:E26. [PMID: 31878100 PMCID: PMC7022958 DOI: 10.3390/biom10010026] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/16/2019] [Accepted: 12/18/2019] [Indexed: 12/23/2022] Open
Abstract
Alignment-free (AF) methodologies have increased in popularity in the last decades as alternative tools to alignment-based (AB) algorithms for performing comparative sequence analyses. They have been especially useful to detect remote homologs within the twilight zone of highly diverse gene/protein families and superfamilies. The most popular alignment-free methodologies, as well as their applications to classification problems, have been described in previous reviews. Despite a new set of graph theory-derived sequence/structural descriptors that have been gaining relevance in the detection of remote homology, they have been omitted as AF predictors when the topic is addressed. Here, we first go over the most popular AF approaches used for detecting homology signals within the twilight zone and then bring out the state-of-the-art tools encoding graph theory-derived sequence/structure descriptors and their success for identifying remote homologs. We also highlight the tendency of integrating AF features/measures with the AB ones, either into the same prediction model or by assembling the predictions from different algorithms using voting/weighting strategies, for improving the detection of remote signals. Lastly, we briefly discuss the efforts made to scale up AB and AF features/measures for the comparison of multiple genomes and proteomes. Alongside the achieved experiences in remote homology detection by both the most popular AF tools and other less known ones, we provide our own using the graphical-numerical methodologies, MARCH-INSIDE, TI2BioP, and ProtDCal. We also present a new Python-based tool (SeqDivA) with a friendly graphical user interface (GUI) for delimiting the twilight zone by using several similar criteria.
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Affiliation(s)
- Guillermin Agüero-Chapin
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Deborah Galpert
- Departamento de Ciencia de la Computación. Universidad Central ¨Marta Abreu¨ de Las Villas (UCLV), Santa Clara 54830, Cuba;
| | - Reinaldo Molina-Ruiz
- Centro de Bioactivos Químicos (CBQ), Universidad Central ¨Marta Abreu¨ de Las Villas (UCLV), Santa Clara 54830, Cuba;
| | - Evys Ancede-Gallardo
- Programa de Doctorado en Fisicoquímica Molecular, Facultad de Ciencias Exactas, Universidad Andrés Bello, Av. República 239, Santiago 8370146, Chile;
| | - Gisselle Pérez-Machado
- EpiDisease S.L. Spin-Off of Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 46980 Valencia, Spain;
| | - Gustavo A. De la Riva
- Laboratorio de Biotecnología Aplicada S. de R.L. de C.V., GRECA Inc., Carretera La Piedad-Carapán, km 3.5, La Piedad, Michoacán 59300, Mexico;
- Tecnológico Nacional de México, Instituto Tecnológico de la Piedad, Av. Ricardo Guzmán Romero, Santa Fe, La Piedad de Cavadas, Michoacán 59370, Mexico
| | - Agostinho Antunes
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
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